<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.4-->
<PhoronixTestSuite>
  <SuiteInformation>
    <Title>Apple M1 Pre Xmas Suite</Title>
    <Version>1.0.0</Version>
    <TestType>System</TestType>
    <Description>Test suite extracted from Apple M1 Pre Xmas.</Description>
    <Maintainer> </Maintainer>
  </SuiteInformation>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu</Arguments>
    <Description>Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/simdjson-1.1.1</Test>
    <Arguments>LargeRandom</Arguments>
    <Description>Throughput Test: LargeRandom</Description>
  </Execute>
  <Execute>
    <Test>pts/simdjson-1.1.1</Test>
    <Arguments>PartialTweets</Arguments>
    <Description>Throughput Test: PartialTweets</Description>
  </Execute>
  <Execute>
    <Test>pts/build2-1.1.0</Test>
    <Description>Time To Compile</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu</Arguments>
    <Description>Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/simdjson-1.1.1</Test>
    <Arguments>Kostya</Arguments>
    <Description>Throughput Test: Kostya</Description>
  </Execute>
  <Execute>
    <Test>pts/simdjson-1.1.1</Test>
    <Arguments>DistinctUserID</Arguments>
    <Description>Throughput Test: DistinctUserID</Description>
  </Execute>
  <Execute>
    <Test>pts/phpbench-1.1.6</Test>
    <Description>PHP Benchmark Suite</Description>
  </Execute>
  <Execute>
    <Test>pts/hmmer-1.2.2</Test>
    <Description>Pfam Database Search</Description>
  </Execute>
  <Execute>
    <Test>pts/build-ffmpeg-1.0.2</Test>
    <Description>Time To Compile</Description>
  </Execute>
  <Execute>
    <Test>pts/sqlite-speedtest-1.0.1</Test>
    <Description>Timed Time - Size 1,000</Description>
  </Execute>
  <Execute>
    <Test>pts/compilebench-1.0.3</Test>
    <Arguments>COMPILE</Arguments>
    <Description>Test: Compile</Description>
  </Execute>
  <Execute>
    <Test>pts/node-web-tooling-1.0.0</Test>
  </Execute>
  <Execute>
    <Test>pts/build-eigen-1.1.0</Test>
    <Description>Time To Compile</Description>
  </Execute>
  <Execute>
    <Test>pts/encode-wavpack-1.4.0</Test>
    <Description>WAV To WavPack</Description>
  </Execute>
  <Execute>
    <Test>pts/mafft-1.6.2</Test>
    <Description>Multiple Sequence Alignment - LSU RNA</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/encode-ape-1.4.0</Test>
    <Description>WAV To APE</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu</Arguments>
    <Description>Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/onednn-1.6.1</Test>
    <Arguments>--deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu</Arguments>
    <Description>Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU</Description>
  </Execute>
  <Execute>
    <Test>pts/compilebench-1.0.3</Test>
    <Arguments>READ_COMPILED_TREE</Arguments>
    <Description>Test: Read Compiled Tree</Description>
  </Execute>
  <Execute>
    <Test>pts/compilebench-1.0.3</Test>
    <Arguments>INITIAL_CREATE</Arguments>
    <Description>Test: Initial Create</Description>
  </Execute>
</PhoronixTestSuite>
