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Scripts to benchmark libraries used by Amun


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Performance Scripts

This repository contains a set of scripts that can be used directly by Amun to check performance of an application stack. This application stack can be supplied directly by user (to verify its stack performance) or can be generated by Dependency Monkey to gather software stack characteristics.


Amun accepts an URL to the script (or you can submit it verbatim as a string) to Amun API as part of the request (parameter script).

To use script directly from GitHub, you can open the given script to view its content and than click on "Raw" button in the GitHub's file header to obtain an URL to a raw script file. Use the URL as the script parameter on Amun API (do not pass directly "non-raw" URL as Amun will download HTML page instead of raw file content).

Follow instructions present at

Example local run

# Clone this repo and cd into tensorflow directory:
$ git clone
$ cd performance/tensorflow
# Install TensorFlow:
$ pipenv install tensorflow==1.9.0 --python 3.6
# Run the performance:
$ pipenv run python3 ./
DTYPE set to float32
DEVICE set to cpu
REPS set to 20000
MATRIX size set to 512
# Version: 1.13.1, path: ['/home/fpokorny/.local/share/virtualenvs/tensorflow-FigIdZQa/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/api', '/home/fpokorny/.local/share/virtualenvs/tensorflow-FigIdZQa/lib/python3.6/site-packages/tensorflow', '/home/fpokorny/.local/share/virtualenvs/tensorflow-FigIdZQa/lib/python3.6/site-packages/tensorflow/_api/v1']
WARNING:tensorflow:From /home/fpokorny/.local/share/virtualenvs/tensorflow-FigIdZQa/lib/python3.6/site-packages/tensorflow/python/framework/ colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
512 x 512 matmul took:        1.2320 ms,       217.67 GFLOPS
  "framework": "tensorflow",
  "name": "PiMatmul",
  "@parameters": {
    "dtype": "float32",
    "device": "cpu",
    "reps": 20000,
    "matrix_size": 512
  "@result": {
    "rate": 0.006877627136404815,
    "elapsed": 38992.12718009949
  "tensorflow_upstream_buildinfo": null,
  "tensorflow_aicoe_buildinfo": {
    "source_HEAD": "6612da89516247503f03ef76e974b51a434fb52e",
    "source_remote_origin": "",
    "OS_VER": "Fedora release 28 (Twenty Eight)",
    "GLIBC_VER": "ldd (GNU libc) 2.27",
    "PIP_VER": "pip 19.0.3 from /usr/local/lib/python3.6/site-packages/pip (python 3.6)",
    "PROTOC_VER": "libprotoc 3.5.0",
    "LOGICAL_CPUS": "64",
    "CORES_PER_PCPU": " 1",
    "PHYSICAL_CPUS": "64",
    "GCC_VER": "gcc (GCC) 8.2.1 20181215 (Red Hat 8.2.1-6)",
    "OS": "Linux",
    "kernel": "3.10.0-862.9.1.el7.x86_64",
    "architecture": "skylake",
    "processor": "Intel Core Processor (Skylake, IBRS)",
    "Bazel_version": "Build label: 0.20.0",
    "Java_version": "1.8.0_201",
    "Python_version": "3.6.8",
    "gpp_version": "g++ (GCC) 8.2.1 20181215 (Red Hat 8.2.1-6)",
    "swig_version": "",
    "NVIDIA_driver_version": "",
    "CUDA_device_count": "0",
    "CUDA_device_names": "",
    "CUDA_toolkit_version": "",
    "GCC_FLAGS": "-march=skylake -mmmx -msse -msse2 -msse3 -mssse3 -mcx16 -msahf -mmovbe -maes -mpclmul -mpopcnt -mabm -mfma -mbmi -mbmi2 -mavx -mavx2 -msse4.2 -msse4.1 -mlzcnt -mrtm -mhle -mrdrnd -mf16c -mfsgsbase -mrdseed -mprfchw -madx -mfxsr -mxsave -mxsaveopt -mavx512f -mavx512cd -mclflushopt -mxsavec -mavx512dq -mavx512bw -mavx512vl -mpku --param l1-cache-size=32 --param l1-cache-line-size=64 --param l2-cache-size=16384 -mtune=skylake",
    "CPUINFO_FLAGS": " fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology eagerfpu pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 arat pku ospke spec_ctrl intel_stibp",
    "CPUINFO_FLAGS_TENSORFLOW": "sse sse2 ssse3 fma sse4_1 sse4_2 avx avx2 avx512f avx512dq avx512cd avx512bw avx512vl ",
    "CPU_FAMILY": "6",
    "CPU_MODEL": "94",
    "GCC_HOST_COMPILER_PATH": "/usr/bin/gcc",
    "CUDA_TOOLKIT_PATH": "/usr/local/cuda",
    "CUDNN_INSTALL_PATH": "/usr/local/cuda",
    "JAVA_HOME": "/usr/lib/jvm/java-1.8.0-openjdk- /usr/lib/jvm/java-1.8.0-openjdk-",
    "PYTHON_LIB_PATH": "/usr/lib64/python3.6/site-packages",
    "LD_LIBRARY_PATH": "/usr/lib64:/usr/local/lib:/usr/local/lib;",
    "PYTHON_BIN_PATH": "/usr/bin/python3.6",
    "PATH": "/home/default/bin:/usr/local/bin:/opt/app-root/src/bin:/opt/app-root/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/home/default/.local/bin",
    "PORT": "",
    "BUILD_OPTS": "",
    "PYTHON_VERSION": "3.6",
    "TEST_WHEEL_FILE": "y",
    "CUSTOM_BUILD": "bazel build --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both --copt=-msse4.2 --cxxopt='-D_GLIBCXX_USE_CXX11_ABI=0' --local_resources 2048,2.0,1.0 --verbose_failures //tensorflow/tools/pip_package:build_pip_package",
    "TF_NEED_TENSORRT": "0",
    "TF_ENABLE_XLA": "0",
    "TF_NEED_VERBS": "0",
    "TF_NEED_S3": "0",
    "TF_CUDA_VERSION": "9.2",
    "TF_CUDA_COMPUTE_CAPABILITIES": "3.0,3.5,5.2,6.0,6.1,7.0",
    "TF_NEED_HDFS": "0",
    "TF_NEED_IGNITE": "0",
    "TF_NEED_GDR": "0",
    "TF_ENABLE_TEST": "0",
    "TF_NEED_GCP": "0",
    "TF_CUDNN_VERSION": "7",
    "TF_NEED_AWS": "0",
    "TF_NEED_ROCM": "0",
    "TF_NEED_OPENCL": "0",
    "TF_GIT_BRANCH": "r1.13",
    "TF_CUDA_CLANG": "0",
    "TF_NEED_JEMALLOC": "1",
    "TF_NEED_KAFKA": "0",
    "TF_NEED_MPI": "0",
    "TF_NEED_CUDA": "0",
    "march": "skylake"

Please note that the JSON output is printed to stdout, other messages go to stderr. Key tensorflow_buildinfo is reported by the script, but is not part of the actual @result. TensorFlow's build information is parsed from custom AICoE TensorFlow builds present on AICoE experimental index.


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