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tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
#!/usr/bin/env python3
# This file is part of the MicroPython project, http://micropython.org/
# The MIT License (MIT)
# Copyright (c) 2019 Damien P. George
import os
import subprocess
import sys
import argparse
from glob import glob
sys.path.append("../tools")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
import pyboard
prepare_script_for_target = __import__("run-tests").prepare_script_for_target
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
# Paths for host executables
if os.name == "nt":
CPYTHON3 = os.getenv("MICROPY_CPYTHON3", "python3.exe")
MICROPYTHON = os.getenv("MICROPY_MICROPYTHON", "../ports/windows/micropython.exe")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
else:
CPYTHON3 = os.getenv("MICROPY_CPYTHON3", "python3")
MICROPYTHON = os.getenv("MICROPY_MICROPYTHON", "../ports/unix/micropython")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
PYTHON_TRUTH = CPYTHON3
BENCH_SCRIPT_DIR = "perf_bench/"
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def compute_stats(lst):
avg = 0
var = 0
for x in lst:
avg += x
var += x * x
avg /= len(lst)
var = max(0, var / len(lst) - avg**2)
return avg, var**0.5
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def run_script_on_target(target, script):
output = b""
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
err = None
if isinstance(target, pyboard.Pyboard):
# Run via pyboard interface
try:
target.enter_raw_repl()
output = target.exec_(script)
except pyboard.PyboardError as er:
err = er
else:
# Run local executable
try:
p = subprocess.run(
target, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, input=script
)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
output = p.stdout
except subprocess.CalledProcessError as er:
err = er
return str(output.strip(), "ascii"), err
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def run_feature_test(target, test):
with open("feature_check/" + test + ".py", "rb") as f:
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
script = f.read()
output, err = run_script_on_target(target, script)
if err is None:
return output
else:
return "CRASH: %r" % err
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def run_benchmark_on_target(target, script):
output, err = run_script_on_target(target, script)
if err is None:
if output == "SKIP":
return -1, -1, "SKIP"
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
time, norm, result = output.split(None, 2)
try:
return int(time), int(norm), result
except ValueError:
return -1, -1, "CRASH: %r" % output
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
else:
return -1, -1, "CRASH: %r" % err
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def run_benchmarks(args, target, param_n, param_m, n_average, test_list):
skip_complex = run_feature_test(target, "complex") != "complex"
skip_native = run_feature_test(target, "native_check") != "native"
target_had_error = False
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
for test_file in sorted(test_list):
print(test_file + ": ", end="")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
# Check if test should be skipped
skip = (
skip_complex
and test_file.find("bm_fft") != -1
or skip_native
and test_file.find("viper_") != -1
)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if skip:
print("SKIP")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
continue
# Create test script
with open(test_file, "rb") as f:
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
test_script = f.read()
with open(BENCH_SCRIPT_DIR + "benchrun.py", "rb") as f:
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
test_script += f.read()
test_script += b"bm_run(%u, %u)\n" % (param_n, param_m)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
# Write full test script if needed
if 0:
with open("%s.full" % test_file, "wb") as f:
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
f.write(test_script)
# Process script through mpy-cross if needed
if isinstance(target, pyboard.Pyboard) or args.via_mpy:
crash, test_script_target = prepare_script_for_target(args, script_text=test_script)
if crash:
print("CRASH:", test_script_target)
continue
else:
test_script_target = test_script
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
# Run MicroPython a given number of times
times = []
scores = []
error = None
result_out = None
for _ in range(n_average):
time, norm, result = run_benchmark_on_target(target, test_script_target)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (<1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a "truth" value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if time < 0 or norm < 0:
error = result
break
if result_out is None:
result_out = result
elif result != result_out:
error = "FAIL self"
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
break
times.append(time)
scores.append(1e6 * norm / time)
# Check result against truth if needed
if error is None and result_out != "None":
test_file_expected = test_file + ".exp"
if os.path.isfile(test_file_expected):
# Expected result is given by a file, so read that in
with open(test_file_expected) as f:
result_exp = f.read().strip()
else:
# Run CPython to work out the expected result
_, _, result_exp = run_benchmark_on_target(PYTHON_TRUTH, test_script)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if result_out != result_exp:
error = "FAIL truth"
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if error is not None:
if not error.startswith("SKIP"):
target_had_error = True
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
print(error)
else:
t_avg, t_sd = compute_stats(times)
s_avg, s_sd = compute_stats(scores)
print(
"{:.2f} {:.4f} {:.2f} {:.4f}".format(
t_avg, 100 * t_sd / t_avg, s_avg, 100 * s_sd / s_avg
)
)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if 0:
print(" times: ", times)
print(" scores:", scores)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
sys.stdout.flush()
return target_had_error
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def parse_output(filename):
with open(filename) as f:
params = f.readline()
n, m, _ = params.strip().split()
n = int(n.split("=")[1])
m = int(m.split("=")[1])
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
data = []
for l in f:
if l.find(": ") != -1 and l.find(": SKIP") == -1 and l.find("CRASH: ") == -1:
name, values = l.strip().split(": ")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
values = tuple(float(v) for v in values.split())
data.append((name,) + values)
return n, m, data
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def compute_diff(file1, file2, diff_score):
# Parse output data from previous runs
n1, m1, d1 = parse_output(file1)
n2, m2, d2 = parse_output(file2)
# Print header
if diff_score:
print("diff of scores (higher is better)")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
else:
print("diff of microsecond times (lower is better)")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if n1 == n2 and m1 == m2:
hdr = "N={} M={}".format(n1, m1)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
else:
hdr = "N={} M={} vs N={} M={}".format(n1, m1, n2, m2)
print(
"{:26} {:>10} -> {:>10} {:>10} {:>7}% (error%)".format(
hdr, file1, file2, "diff", "diff"
)
)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
# Print entries
while d1 and d2:
if d1[0][0] == d2[0][0]:
# Found entries with matching names
entry1 = d1.pop(0)
entry2 = d2.pop(0)
name = entry1[0].rsplit("/")[-1]
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
av1, sd1 = entry1[1 + 2 * diff_score], entry1[2 + 2 * diff_score]
av2, sd2 = entry2[1 + 2 * diff_score], entry2[2 + 2 * diff_score]
sd1 *= av1 / 100 # convert from percent sd to absolute sd
sd2 *= av2 / 100 # convert from percent sd to absolute sd
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
av_diff = av2 - av1
sd_diff = (sd1**2 + sd2**2) ** 0.5
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
percent = 100 * av_diff / av1
percent_sd = 100 * sd_diff / av1
print(
"{:26} {:10.2f} -> {:10.2f} : {:+10.2f} = {:+7.3f}% (+/-{:.2f}%)".format(
name, av1, av2, av_diff, percent, percent_sd
)
)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
elif d1[0][0] < d2[0][0]:
d1.pop(0)
else:
d2.pop(0)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
def main():
cmd_parser = argparse.ArgumentParser(description="Run benchmarks for MicroPython")
cmd_parser.add_argument(
"-t", "--diff-time", action="store_true", help="diff time outputs from a previous run"
)
cmd_parser.add_argument(
"-s", "--diff-score", action="store_true", help="diff score outputs from a previous run"
)
cmd_parser.add_argument(
"-p", "--pyboard", action="store_true", help="run tests via pyboard.py"
)
cmd_parser.add_argument(
"-d", "--device", default="/dev/ttyACM0", help="the device for pyboard.py"
)
cmd_parser.add_argument("-a", "--average", default="8", help="averaging number")
cmd_parser.add_argument(
"--emit", default="bytecode", help="MicroPython emitter to use (bytecode or native)"
)
cmd_parser.add_argument("--via-mpy", action="store_true", help="compile code to .mpy first")
cmd_parser.add_argument("--mpy-cross-flags", default="", help="flags to pass to mpy-cross")
cmd_parser.add_argument("N", nargs=1, help="N parameter (approximate target CPU frequency)")
cmd_parser.add_argument("M", nargs=1, help="M parameter (approximate target heap in kbytes)")
cmd_parser.add_argument("files", nargs="*", help="input test files")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
args = cmd_parser.parse_args()
if args.diff_time or args.diff_score:
compute_diff(args.N[0], args.M[0], args.diff_score)
sys.exit(0)
# N, M = 50, 25 # esp8266
# N, M = 100, 100 # pyboard, esp32
# N, M = 1000, 1000 # PC
N = int(args.N[0])
M = int(args.M[0])
n_average = int(args.average)
if args.pyboard:
if not args.mpy_cross_flags:
args.mpy_cross_flags = "-march=armv7m"
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
target = pyboard.Pyboard(args.device)
target.enter_raw_repl()
else:
target = [MICROPYTHON, "-X", "emit=" + args.emit]
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if len(args.files) == 0:
tests_skip = ("benchrun.py",)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if M <= 25:
# These scripts are too big to be compiled by the target
tests_skip += ("bm_chaos.py", "bm_hexiom.py", "misc_raytrace.py")
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
tests = sorted(
BENCH_SCRIPT_DIR + test_file
for test_file in os.listdir(BENCH_SCRIPT_DIR)
if test_file.endswith(".py") and test_file not in tests_skip
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
)
else:
tests = sorted(args.files)
print("N={} M={} n_average={}".format(N, M, n_average))
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
target_had_error = run_benchmarks(args, target, N, M, n_average, tests)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if isinstance(target, pyboard.Pyboard):
target.exit_raw_repl()
target.close()
if target_had_error:
sys.exit(1)
tests: Add performance benchmarking test-suite framework. This benchmarking test suite is intended to be run on any MicroPython target. As such all tests are parameterised with N and M: N is the approximate CPU frequency (in MHz) of the target and M is the approximate amount of heap memory (in kbytes) available on the target. When running the benchmark suite these parameters must be specified and then each test is tuned to run on that target in a reasonable time (&lt;1 second). The test scripts are not standalone: they require adding some extra code at the end to run the test with the appropriate parameters. This is done automatically by the run-perfbench.py script, in such a way that imports are minimised (so the tests can be run on targets without filesystem support). To interface with the benchmarking framework, each test provides a bm_params dict and a bm_setup function, with the later taking a set of parameters (chosen based on N, M) and returning a pair of functions, one to run the test and one to get the results. When running the test the number of microseconds taken by the test are recorded. Then this is converted into a benchmark score by inverting it (so higher number is faster) and normalising it with an appropriate factor (based roughly on the amount of work done by the test, eg number of iterations). Test outputs are also compared against a &#34;truth&#34; value, computed by running the test with CPython. This provides a basic way of making sure the test actually ran correctly. Each test is run multiple times and the results averaged and standard deviation computed. This is output as a summary of the test. To make comparisons of performance across different runs the run-perfbench.py script also includes a diff mode that reads in the output of two previous runs and computes the difference in performance. Reports are given as a percentage change in performance with a combined standard deviation to give an indication if the noise in the benchmarking is less than the thing that is being measured. Example invocations for PC, pyboard and esp8266 targets respectively: $ ./run-perfbench.py 1000 1000 $ ./run-perfbench.py --pyboard 100 100 $ ./run-perfbench.py --pyboard --device /dev/ttyUSB0 50 25
5 years ago
if __name__ == "__main__":
main()