78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
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################################################################################
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# Copyright (c) 2015-2018 Skymind, Inc.
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#
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# This program and the accompanying materials are made available under the
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# terms of the Apache License, Version 2.0 which is available at
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# https://www.apache.org/licenses/LICENSE-2.0.
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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################################################################################
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import jumpy as jp
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import numpy as np
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from random import randint
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import timeit
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import gc
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gc.disable()
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jp.disable_gc()
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class Benchmark(object):
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def __init__(self, n=1000):
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print 'Running tests with [', n, 'x', n, '] dimensionality'
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self.n = n
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self.m = 200
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self.np_arr = []
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self.nd4j_arr = []
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for counter in range(0, self.m + 1):
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self.np_arr.append(np.linspace(1, n * n, n * n).reshape((n, n)))
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for counter in range(0, self.m + 1):
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self.nd4j_arr.append(jp.array(self.np_arr[counter]))
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def run_nd4j_scalar(self):
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self.nd4j_arr[randint(0, self.m)] += 1.0172
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def run_numpy_scalar(self):
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self.np_arr[randint(0, self.m)] += 1.0172
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def run_nd4j_add(self):
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self.nd4j_arr[randint(0, self.m)] += self.nd4j_arr[randint(0, self.m)]
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def run_numpy_add(self):
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self.np_arr[randint(0, self.m)] += self.np_arr[randint(0, self.m)]
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def run_numpy_sub(self):
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self.np_arr[randint(0, self.m)] -= self.np_arr[randint(0, self.m)]
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def run_nd4j_sub(self):
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self.nd4j_arr[randint(0, self.m)] -= self.nd4j_arr[randint(0, self.m)]
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def run_nd4j_mmul(self):
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jp.dot(self.nd4j_arr[randint(0, self.m)], self.nd4j_arr[randint(0, self.m)])
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def run_numpy_mmul(self):
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np.dot(self.np_arr[randint(0, self.m)], self.np_arr[randint(0, self.m)])
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def run_benchmark(self, n_trials=1000):
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print 'nd4j scalar ', timeit.timeit(self.run_nd4j_scalar, number=n_trials)
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print 'numpy scalar ', timeit.timeit(self.run_numpy_scalar, number=n_trials)
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print 'nd4j add ', timeit.timeit(self.run_nd4j_add, number=n_trials)
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print 'numpy add ', timeit.timeit(self.run_numpy_add, number=n_trials)
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print 'nd4j sub ', timeit.timeit(self.run_nd4j_sub, number=n_trials)
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print 'numpy sub ', timeit.timeit(self.run_numpy_sub, number=n_trials)
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print 'nd4j mmul ', timeit.timeit(self.run_nd4j_mmul, number=n_trials)
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print 'numpy mmul ', timeit.timeit(self.run_numpy_mmul, number=n_trials)
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benchmark = Benchmark()
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benchmark.run_benchmark()
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