MKL-DNN DNNL 1.1.1

pts/mkl-dnn-1.1.1 - 03 October 2019 - Update data types.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.0.0-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>https://github.com/intel/mkl-dnn/archive/v1.1.tar.gz</URL>
      <MD5>dfe923c85a0f43fc82d018bc5e4ac345</MD5>
      <SHA256>c5aac67e5ed4d95fe9943f835df49bbe6d608507780787c64aa620bdbd2171ba</SHA256>
      <FileName>mkl-dnn-1.1.tar.gz</FileName>
      <FileSize>4514572</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/sh

tar -xf mkl-dnn-1.1.tar.gz

cd mkl-dnn-1.1/
#./scripts/prepare_mkl.sh 

mkdir build 
cd build 
CFLAGS="-O3 -march=native" CXXFLAGS="-O3 -march=native" cmake -DCMAKE_BUILD_TYPE=Release MKLDNN_ARCH_OPT_FLAGS="-O3 -march=native" $CMAKE_OPTIONS ..
make -j $NUM_CPU_CORES
echo $? > ~/install-exit-status

cd ~

echo "#!/bin/bash
export OMP_PLACES=cores
export OMP_PROC_BIND=close
cd mkl-dnn-1.1/build/tests/benchdnn
./benchdnn --mode=p \$1 \$3 \$2 > \$LOG_FILE 2>&1
echo \$? > ~/test-exit-status" > mkl-dnn
chmod +x mkl-dnn

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.0.0-->
<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 v9.0.0-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>MKL-DNN DNNL</Title>
    <AppVersion>1.1</AppVersion>
    <Description>This is a test of the Intel MKL-DNN (DNNL / Deep Neural Network Library) as an Intel-optimized 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>1.1.1</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>
    <Option>
      <DisplayName>Harness</DisplayName>
      <Identifier>harness</Identifier>
      <Menu>
        <Entry>
          <Name>Convolution Batch conv_all</Name>
          <Value>--conv --batch=inputs/conv/conv_all</Value>
        </Entry>
        <Entry>
          <Name>Convolution Batch conv_3d</Name>
          <Value>--conv --batch=inputs/conv/conv_3d</Value>
        </Entry>
        <Entry>
          <Name>Convolution Batch conv_alexnet</Name>
          <Value>--conv --batch=inputs/conv/conv_alexnet</Value>
        </Entry>
        <Entry>
          <Name>Convolution Batch conv_googlenet_v3</Name>
          <Value>--conv --batch=inputs/conv/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>
        <Entry>
          <Name>Recurrent Neural Network Training</Name>
          <Value>--rnn --batch=inputs/rnn/rnn_training</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>u8s8f32</Name>
          <Value>u8s8f32</Value>
          <Message>Optimized For AVX-512</Message>
        </Entry>
        <Entry>
          <Name>bf16bf16bf16</Name>
          <Value>bf16bf16bf16</Value>
          <Message>Optimized For AVX-512 + VNNI</Message>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>