Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Windows*

ID 766692
Date 11/07/2023

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Document Table of Contents

Running the Intel® Distribution for LINPACK* Benchmark and the Intel® Optimized HPL-AI* Benchmark

To run the Intel® Distribution for LINPACK Benchmark on multiple nodes or on one node with multiple MPI processes, you need to use MPI and either modify HPL.dat or use Ease-of-use Command-line Parameters. The following example describes how to run the dynamically-linked prebuilt Intel® Distribution for LINPACK Benchmark binary using the script provided. To run other binaries, adjust the steps accordingly.

  1. Load the necessary environment variables by accessing setup scripts.

  2. In HPL.dat, set the problem size N to 10000. Because this setting is for a test run, the problem size should be small.

  3. For better performance, enable non-uniform memory access (NUMA) on your system and configure to run an MPI process for each NUMA socket as explained below.

    • Refer to your BIOS settings to enable NUMA on your system.

    • Set the following variables at the top of the runme_intel64_dynamic.bat script according to your cluster configuration:

      The total number of MPI processes.
      The number of MPI processes per each cluster node.
      The number of NUMA nodes per each MPI process.
      Uncomment this to enable the Intel® Optimized HPL-AI* Benchmark.
      Uncomment this to enable GPUs.
      The number of stacks on each GPU.
      The number of GPUs.
    • In the HPL.dat file, set the parameters Ps and Qs so that Ps * Qs equals the number of MPI processes. For example, for two processes, set Ps to 1 and Qs to 2. Alternatively, leave the HPL.dat file as is and launch with the -p and -q command-line parameters.

  4. Execute the runme_intel64_dynamic.bat script:


  5. Rerun the test, increasing the size of the problem until the matrix size uses about 80% of the available memory. To do this, either modify Ns in line 6 of HPL.dat or use the -n command-line parameter:

    • For 16 GB: 40000 Ns

    • For 32 GB: 56000 Ns

    • For 64 GB: 83000 Ns

Product and Performance Information

Performance varies by use, configuration and other factors. Learn more at

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