graalpython/mx.graalpython/mx_graalpython_bench_param.py at master · srctkrs/graalpython · GitHub

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# Redistribution and use in source and binary forms, with or without modification, are

# permitted provided that the following conditions are met:

# 1. Redistributions of source code must retain the above copyright notice, this list of

# conditions and the following disclaimer.

# 2. Redistributions in binary form must reproduce the above copyright notice, this list of

# conditions and the following disclaimer in the documentation and/or other materials provided

# with the distribution.

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS

# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF

# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE

# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,

# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE

# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED

# AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING

# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED

# OF THE POSSIBILITY OF SUCH DAMAGE.

import os

import mx

_graalpython_suite = mx.suite('graalpython')

py = ".py"

_BASE_PATH = os.path.join(_graalpython_suite.dir, 'graalpython', 'com.oracle.graal.python.benchmarks', 'python')

HARNESS_PATH = os.path.join(_BASE_PATH, 'harness.py')

PATH_BENCH = os.path.join(_BASE_PATH, 'com.oracle.graal.python.benchmarks')

PATH_MICRO = os.path.join(_BASE_PATH, 'micro')

PATH_MESO = os.path.join(_BASE_PATH, 'meso')

PATH_MACRO = os.path.join(_BASE_PATH, 'macro')

PATH_WARMUP = os.path.join(_BASE_PATH, 'warmup')

PATH_INTEROP = os.path.join(_BASE_PATH, 'host_interop')

PATH_JAVA_EMBEDDING = os.path.join(_BASE_PATH, 'java-embedding')

PATH_HEAP = os.path.join(_BASE_PATH, 'heap')

# ----------------------------------------------------------------------------------------------------------------------

# the python micro benchmarks

# ----------------------------------------------------------------------------------------------------------------------

# the argument list contains both the harness and benchmark args

ITER_100 = ['-i', '100']

ITER_50 = ['-i', '50']

ITER_35 = ['-i', '35']

ITER_25 = ['-i', '25']

ITER_20 = ['-i', '20']

ITER_15 = ['-i', '15']

ITER_10 = ['-i', '10']

ITER_6 = ['-i', '6']

ITER_5 = ['-i', '5']

WARMUP_2 = ['-w', '2']

MICRO_BENCHMARKS = {

'arith-binop': ITER_10 + ['5'],

'arith-modulo-sized': ITER_10 + ['500'],

'attribute-access-polymorphic': ITER_10 + ['1000'],

'attribute-access': ITER_10 + ['5000'],

'attribute-access-super': ITER_10 + ['5_000'],

'attribute-bool': ITER_10 + ['3000'],

'boolean-logic-sized': ITER_10 + ['5_000'],

'builtin-len-tuple-sized': ITER_10 + ['1_000_000_000'],

'builtin-len': ITER_10 + [],

'call-method-polymorphic': ITER_10 + ['1000'],

'for-range': ITER_15 + ['50000'],

'function-call-sized': ITER_10 + ['2_000_000_000'],

'generator-expression-sized': ITER_10 + ['30_000'],

'generator-notaligned-sized': ITER_10 + ['30_000'],

'generator-sized': ITER_10 + ['30_000'],

'genexp-builtin-call-sized': ITER_10 + ['50_000'],

'list-comp': ITER_10 + ['5000'],

'list-indexing': ITER_10 + ['1000000'],

'list-indexing-from-constructor': ITER_10 + ['10000000'],

'list-indexing-from-literal': ITER_10 + ['10000000'],

'list-iterating-explicit': ITER_10 + ['1000000'],

'list-iterating': ITER_10 + ['1000000'],

'list-iterating-obj-sized': ITER_10 + ['100_000_000'],

'list-constructions-sized': ITER_10 + ['10_000'],

'list-sort-objects': ITER_10 + ['10_000'],

'list-sort-strings': ITER_10 + ['500_000'],

'list-sort-keyed': ITER_10 + ['50_000'],

'dict-getitem-sized': ITER_10 + ['50_000_000'],

'math-sqrt': ITER_10 + ['500000000'],

'object-allocate': ITER_10 + ['5000'],

'object-layout-change': ITER_10 + ['1000000'],

'special-add-int-sized': ITER_10 + ['20_000'],

'special-add-sized': ITER_10 + ['20_000'],

'special-len': ITER_10 + ['5'],

'member-access': ITER_10 + ['5000'],

'magic-bool-sized': ITER_10 + ['300_000_000'],

'magic-iter': ITER_10 + ['50000000'],

'call-classmethod-sized': ITER_10 + ['500_000_000'],

'mmap-anonymous-sized': ITER_10 + ['20_000'],

'mmap-file': ITER_10 + ['1000'],

'generate-functions-sized': ITER_15 + ['500_000_000'],

'try-except-simple': ITER_10 + ['500_000_000'],

'try-except-store-simple': ITER_10 + ['500_000_000'],

'try-except-store-two-types': ITER_10 + ['100_000_000'],

'try-except-two-types': ITER_10 + ['100_000_000'],

'with-exception': ITER_10 + ['100_000_000'],

'tuple-indexing-from-constructor': ITER_10 + ['10000000'],

'tuple-indexing-from-literal': ITER_10 + ['10000000'],

'repeated-import': ITER_10 + ['10000000'],

'codeobject-interpretation': ITER_10 + ['2000'],

'regexp': ITER_10,

'regexp-split': ITER_10,

'regexp-literal-no-match': ITER_10,

'regexp-literal-match': ITER_10,

'regexp-universal-match': ITER_10,

'regexp-char-class-match': ITER_10,

'regexp-char-class-no-match': ITER_10,

'virtualize-in-try-catch-oom': ITER_10,

}

MICRO_BENCHMARKS_SMALL = {

'arith-modulo-sized': ITER_6 + WARMUP_2 + ['1'],

'if-generic': ITER_10 + WARMUP_2 + ['500000'],

'if-generic-non-builtin': ITER_10 + WARMUP_2 + ['500000'],

'if-polymorph': ITER_10 + WARMUP_2 + ['1000000'],

'attribute-access-polymorphic': ITER_6 + WARMUP_2 + ['20'],

'attribute-access': ITER_6 + WARMUP_2 + ['100'],

'attribute-access-super': ITER_6 + WARMUP_2 + ['40'],

'attribute-bool': ITER_6 + WARMUP_2 + ['2'],

'boolean-logic-sized': ITER_6 + WARMUP_2 + ['10'],

'builtin-len-tuple-sized': ITER_6 + WARMUP_2 + ['10_000_000'],

'builtin-len': ITER_10 + WARMUP_2 + ['2_500_000'],

'bytecode-benchmark': ITER_6 + WARMUP_2 + ['500'],

'call-method-polymorphic': ITER_6 + WARMUP_2 + ['10'],

'for-range': ITER_10 + WARMUP_2 + WARMUP_2 + ['50'],

'function-call-sized': ITER_10 + WARMUP_2 + ['2_000_000'],

'generator-expression-sized': ITER_10 + WARMUP_2 + ['3000', '500'],

'generator-notaligned-sized': ITER_10 + WARMUP_2 + ['3000', '500'],

'generator-sized': ITER_10 + WARMUP_2 + ['3000', '500'],

'genexp-builtin-call-sized': ITER_10 + WARMUP_2 + ['3000', '500'],

'list-comp': ITER_15 + WARMUP_2 + ['25'],

'list-indexing': ITER_6 + WARMUP_2 + ['10_000'],

'list-indexing-from-constructor': ITER_6 + WARMUP_2 + ['250_000'],

'list-indexing-from-literal': ITER_6 + WARMUP_2 + ['250_000'],

'list-iterating-explicit': ITER_6 + WARMUP_2 + ['10_000'],

'list-iterating': ITER_6 + WARMUP_2 + ['25_000'],

'list-iterating-obj-sized': ITER_6 + WARMUP_2 + ['1_000_000'],

'list-constructions-sized': ITER_15 + WARMUP_2 + ['500'],

'dict-getitem-sized': ITER_10 + WARMUP_2 + ['1_000_000'],

'math-sqrt': ITER_6 + WARMUP_2 + ['20_000_000'],

'object-allocate': ITER_6 + WARMUP_2 + ['50'],

'object-layout-change': ITER_6 + WARMUP_2 + ['10_000'],

'special-add-int-sized': ITER_6 + WARMUP_2 + ['1_000'],

'special-add-sized': ITER_6 + WARMUP_2 + ['1_000'],

'special-len': ITER_10 + WARMUP_2 + ['1', '1_000'],

'member-access': ITER_10 + WARMUP_2 + ['10'],

'magic-bool-sized': ITER_10 + WARMUP_2 + ['1_000_000'],

'magic-iter': ITER_6 + WARMUP_2 + ['250000'],

'call-classmethod-sized': ITER_10 + WARMUP_2 + ['1_000_000'],

'mmap-anonymous-sized': ITER_6 + WARMUP_2 + ['1_000'],

'mmap-file': ITER_15 + WARMUP_2 + ['100'],

'generate-functions-sized': ITER_10 + WARMUP_2 + ['1_000_000'],

'try-except-simple': ITER_6 + WARMUP_2 + ['2_500_000'],

'try-except-store-simple': ITER_6 + WARMUP_2 + ['2_000_000'],

'try-except-store-two-types': ITER_10 + WARMUP_2 + ['1_000_000'],

'try-except-two-types': ITER_10 + WARMUP_2 + ['1_000_000'],

'with-exception': ITER_6 + WARMUP_2 + ['5_000_000'],

'tuple-indexing-from-constructor': ITER_6 + WARMUP_2 + ['250_000'],

'tuple-indexing-from-literal': ITER_6 + WARMUP_2 + ['400_000'],

}

def _pickling_benchmarks(module='pickle'):

return {

'{}-strings'.format(module): ITER_20 + ['4'],

'{}-lists'.format(module): ITER_20 + ['4'],

'{}-dicts'.format(module): ITER_20 + ['4'],

'{}-objects'.format(module): ITER_20 + ['20'],

'{}-funcs'.format(module): ITER_20 + ['30'],

}

# MICRO_BENCHMARKS.update(_pickling_benchmarks('pickle'))

# MICRO_BENCHMARKS.update(_pickling_benchmarks('cPickle'))

MICRO_NATIVE_BENCHMARKS = {

'c-member-access': ITER_5 + ['10000'],

'c-list-iterating-obj': ITER_5 + ['50000000'],

'c-magic-bool': ITER_5 + ['100000000'],

'c-magic-iter': ITER_5 + ['50000000'],

'c-arith-binop': ITER_5 + ['1000'],

'c-arith-binop2': ITER_5 + ['100'],

'c-call-classmethod': ITER_5 + ['50000000'],

'c-issubtype-polymorphic-forced-to-native': ITER_5 + ['50000000'],

'c-issubtype-polymorphic': ITER_5 + ['50000000'],

'c-issubtype-monorphic': ITER_5 + ['50000000'],

'c-call-method': ITER_5 + ['5000000'],

'c-instantiate-large': ITER_5 + ['1000'],

}

MESO_BENCHMARKS = {

# -------------------------------------------------------

# generator benchmarks

# -------------------------------------------------------

'euler31': ITER_10 + ['200'],

'euler11': ITER_10 + ['10000'],

'ai-nqueen': ITER_10 + ['10'],

'pads-eratosthenes': ITER_10 + ['100000'],

'pads-integerpartitions': ITER_10 + ['700'],

'pads-bipartite-sized': ITER_10 + ['100_000'],

'pads-lyndon': ITER_15 + ['10000000'],

# -------------------------------------------------------

# object benchmarks

# -------------------------------------------------------

'richards3': ITER_10 + ['200'],

'bm-float': ITER_10 + ['1000'],

# -------------------------------------------------------

# normal benchmarks

# -------------------------------------------------------

'binarytrees3': ITER_25 + ['18'],

'fannkuchredux3': ITER_10 + ['11'],

'fasta3': ITER_10 + ['25000000'],

'mandelbrot3': ITER_10 + ['4000'],

'meteor3': ITER_15 + ['2098'],

'nbody3': ITER_10 + ['5000000'],

'spectralnorm3': ITER_10 + ['3000'],

'pidigits': ITER_10 + [],

'sieve-sized': ITER_15 + ['500_000'],

'image-magix-sized2': ITER_10 + ['30000'],

'parrot-b2': ITER_10 + ['200'],

'threadring': ITER_25 + ['100_000_000'],

'regexdna-sized2': ITER_25 + ['4'],

'knucleotide': ITER_25 + [],

'chaos-sized2': ITER_10 + ['500'],

'go-sized2': ITER_15 + ['50'],

'raytrace-simple': ITER_10 + [],

'lud-sized2': ITER_10 + ['1536'],

'mm-sized2': ITER_15 + ['350'],

# Rodinia

'backprop_rodinia-sized2': ITER_15 + ['8388608'],

'lavaMD_rodinia-sized2': ITER_15 + ['48'],

'pathfinder_rodinia-sized2': ITER_25 + ['50'],

'particlefilter_rodinia': ITER_10 + ['2048'],

'srad_rodinia-sized2': ITER_15 + ['1000'],

}

MESO_BENCHMARKS_SMALL = {

'pads-eratosthenes': ITER_20 + WARMUP_2 + ['2000'],

'richards3': ITER_10 + WARMUP_2 + ['2'],

'chaos': ITER_15 + WARMUP_2 + ['2'],

'image-magix': ITER_15 + WARMUP_2 + ['100'],

'raytrace-simple': ITER_10 + WARMUP_2 + ['110', '110'],

}

MACRO_BENCHMARKS = {

'gcbench': ITER_10 + ['10'],

}

WARMUP_BENCHMARKS = {

'gcbench': ITER_100 + ["--startup=1,10,100"] + ['10'],

'binarytrees3': ITER_100 + ["--startup=1,10,100"] + ['18'],

'pads-integerpartitions': ITER_100 + ["--startup=1,10,100"] + ['700'],

}

INTEROP_BENCHMARKS = {

'euler_java': ITER_10 + ['200'],

'image-magix': ITER_10 + ['10000'],

'image-magix-java': ITER_10 + ['10000'],

'java-register': ITER_10 + ['2_000'],

}

JAVA_EMBEDDING_MESO_BENCHMARKS = {

'chaos': ITER_6 + WARMUP_2 + [],

'richards3': ITER_6 + WARMUP_2 + [],

'image-magix': ITER_6 + WARMUP_2 + [],

'raytrace-simple': ITER_6 + WARMUP_2 + [],

}

JAVA_EMBEDDING_MESO_BENCHMARKS_SMALL = {

'chaos': ITER_6 + WARMUP_2 + ['--', '1'],

'richards3': ITER_6 + WARMUP_2 + ['--', '2'],

'image-magix': ITER_6 + WARMUP_2 + ['--', '100'],

'raytrace-simple': ITER_6 + WARMUP_2 + ['--', '110', '110'],

}

# NOTE: there are also JMH benchmarks, which are based on JMH support in MX

# see PythonJMHDistMxBenchmarkSuite

# -------------------------------------------------------

# Parameters for parsing benchmarks

# 1. full qualified name of class that define the benchmark

# 2. -i number : is number of benchmark iterations (defualt 5)

# 3. -w number : is number of warmup iterations, in these cases should not be necessary (default 0)

# 4. -n number : is number of parsing cycles of on file in one iteration (default 1)

# 5. -r : if it's present, then folders on the defined paths are parsed recursively

# 6. : files or directories, that will be processed

# 7. -e : after this mark all the listed files and directories are excluded from the benchmark

# 8. : files or directories that will be excluded from the benchmark

# -------------------------------------------------------

_PARSER_JAVA_PACKAGE = 'com.oracle.graal.python.benchmarks.parser.'

PATH_RUNTIME_FILES_PARSER_TESTS = os.path.join(_graalpython_suite.dir, 'graalpython', 'com.oracle.graal.python.test', 'testData', 'testFiles', 'RuntimeFileTests')

PATH_PYTHON_LIB = os.path.join(_graalpython_suite.dir, 'graalpython', 'lib-python', '3')

# ----------------------------------------------------------------------------------------------------------------------

# the benchmarks

# ----------------------------------------------------------------------------------------------------------------------

BENCHMARKS = {

"micro": [PATH_MICRO, MICRO_BENCHMARKS],

"micro-native": [PATH_MICRO, MICRO_NATIVE_BENCHMARKS],

"meso": [PATH_MESO, MESO_BENCHMARKS],

"macro": [PATH_MACRO, MACRO_BENCHMARKS],

"interop": [PATH_INTEROP, INTEROP_BENCHMARKS],

"micro-small": [PATH_MICRO, MICRO_BENCHMARKS_SMALL],

"meso-small": [PATH_MESO, MESO_BENCHMARKS_SMALL],

}

JAVA_DRIVER_BENCHMARKS = {

"java-embedding-meso": [PATH_MESO, JAVA_EMBEDDING_MESO_BENCHMARKS],

"java-embedding-meso-small": [PATH_MESO, JAVA_EMBEDDING_MESO_BENCHMARKS_SMALL],

}

WARMUP_BENCHMARKS = {

"python-warmup": [PATH_WARMUP, WARMUP_BENCHMARKS],

}

HEAP_BENCHMARKS = {

"heap": [PATH_HEAP, {

"post-startup": [],

"import-a-lot": [],

"allocate-objects": [],

}]

}

graalpython/mx.graalpython/mx_graalpython_bench_param.py at master · srctkrs/graalpython · GitHub

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