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MKL-DNN 1.0.1
pts/mkl-dnn-1.0.1
- 17 April 2019 -
MKLDNN_ARCH_OPT_FLAGS="-O3 $CXXFLAGS"
downloads.xml
<?xml version="1.0"?> <!--Phoronix Test Suite v8.8.0m2--> <PhoronixTestSuite> <Downloads> <Package> <URL>http://www.phoronix-test-suite.com/benchmark-files/mkl-dnn-20190416.tar.xz</URL> <MD5>a6dba314d25c1201d01d9041c50f8ede</MD5> <SHA256>9cb03a8a6515c2810a416c46a3648420d6ecf3ce373a9238881aa0e9d3bbbdb4</SHA256> <FileName>mkl-dnn-20190416.tar.xz</FileName> <FileSize>1147600</FileSize> </Package> </Downloads> </PhoronixTestSuite>
install.sh
#!/bin/sh tar -xf mkl-dnn-20190416.tar.xz cd mkl-dnn-master/ ./scripts/prepare_mkl.sh mkdir build cd build cmake -DCMAKE_BUILD_TYPE=Release MKLDNN_ARCH_OPT_FLAGS="-O3 $CXXFLAGS" $CMAKE_OPTIONS .. make -j $NUM_CPU_CORES echo $? > ~/install-exit-status cd ~ echo "#!/bin/sh cd mkl-dnn-master/build/tests/benchdnn ./benchdnn \$@ > \$LOG_FILE 2>&1 echo \$? > ~/test-exit-status" > mkl-dnn chmod +x mkl-dnn
results-definition.xml
<?xml version="1.0"?> <!--Phoronix Test Suite v8.8.0m2--> <PhoronixTestSuite> <ResultsParser> <OutputTemplate>total perf min(ms) #_MIN_RESULT_# avg(ms) #_RESULT_#</OutputTemplate> <LineHint>total perf</LineHint> <TurnCharsToSpace>:</TurnCharsToSpace> </ResultsParser> </PhoronixTestSuite>
test-definition.xml
<?xml version="1.0"?> <!--Phoronix Test Suite v8.8.0m2--> <PhoronixTestSuite> <TestInformation> <Title>MKL-DNN</Title> <AppVersion>2019-04-16</AppVersion> <Description>This is a test of the Intel MKL-DNN as the Intel Math Kernel Library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported.</Description> <ResultScale>ms</ResultScale> <Proportion>LIB</Proportion> <TimesToRun>3</TimesToRun> </TestInformation> <TestProfile> <Version>1.0.1</Version> <SupportedPlatforms>Linux</SupportedPlatforms> <SoftwareType>Utility</SoftwareType> <TestType>Processor</TestType> <License>Free</License> <Status>Verified</Status> <ExternalDependencies>build-utilities, cmake</ExternalDependencies> <EnvironmentSize>355</EnvironmentSize> <ProjectURL>https://github.com/intel/mkl-dnn</ProjectURL> <InternalTags>SMP, OpenMP</InternalTags> <Maintainer>Michael Larabel</Maintainer> </TestProfile> <TestSettings> <Default> <Arguments>--mode=p</Arguments> </Default> <Option> <DisplayName>Harness</DisplayName> <Identifier>harness</Identifier> <Menu> <Entry> <Name>Convolution Batch conv_all</Name> <Value>--conv --batch=inputs/conv_all</Value> </Entry> <Entry> <Name>Convolution Batch conv_3d</Name> <Value>--conv --batch=inputs/conv_3d</Value> </Entry> <Entry> <Name>Convolution Batch conv_alexnet</Name> <Value>--conv --batch=inputs/conv_alexnet</Value> </Entry> <Entry> <Name>Convolution Batch conv_googlenet_v3</Name> <Value>--conv --batch=inputs/conv_googlenet_v3</Value> </Entry> <Entry> <Name>Deconvolution Batch deconv_all</Name> <Value>--deconv --batch=inputs/deconv/deconv_all</Value> </Entry> <Entry> <Name>Deconvolution Batch deconv_1d</Name> <Value>--deconv --batch=inputs/deconv/deconv_1d</Value> </Entry> <Entry> <Name>Deconvolution Batch deconv_3d</Name> <Value>--deconv --batch=inputs/deconv/deconv_3d</Value> </Entry> <Entry> <Name>IP Batch 1D</Name> <Value>--ip --batch=inputs/ip/ip_1d</Value> </Entry> <Entry> <Name>IP Batch All</Name> <Value>--ip --batch=inputs/ip/ip_all</Value> </Entry> </Menu> </Option> <Option> <DisplayName>Data Type</DisplayName> <Identifier>data-type</Identifier> <ArgumentPrefix>--cfg=</ArgumentPrefix> <Menu> <Entry> <Name>f32</Name> <Value>f32</Value> </Entry> <Entry> <Name>u8s8f32s32</Name> <Value>u8s8f32s32</Value> </Entry> <Entry> <Name>u8s8u8s32</Name> <Value>u8s8u8s32</Value> </Entry> </Menu> </Option> </TestSettings> </PhoronixTestSuite>