Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Windows*

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ID 766692
Date 12/16/2022
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Document Table of Contents
Document Table of Contents x
Developer Guide for Intel® oneAPI Math Kernel Library for Windows*
Developer Guide for Intel® oneAPI Math Kernel Library for Windows* x
Getting Help and Support What's New Notational Conventions Related Information Getting Started Structure of the Intel® oneAPI Math Kernel Library Linking Your Application with the Intel® oneAPI Math Kernel Library Managing Performance and Memory Language-specific Usage Options Obtaining Numerically Reproducible Results Coding Tips Managing Output Working with the Intel® oneAPI Math Kernel Library Cluster Software Managing Behavior of the Intel® oneAPI Math Kernel Library with Environment Variables Programming with Intel® Math Kernel Library in Integrated Development Environments (IDE) Intel® oneAPI Math Kernel Library Benchmarks Appendix A: Intel® oneAPI Math Kernel Library Language Interfaces Support Appendix B: Support for Third-Party Interfaces Appendix C: Directory Structure in Detail Notices and Disclaimers
Getting Started x
Shared Library Versioning CMake Config for oneMKL Checking Your Installation Setting Environment Variables Compiler Support Using Code Examples What You Need to Know Before You Begin Using the Intel® oneAPI Math Kernel Library
Structure of the Intel® oneAPI Math Kernel Library x
Architecture Support High-level Directory Structure Layered Model Concept
Linking Your Application with the Intel® oneAPI Math Kernel Library x
Linking Quick Start Linking Examples Linking in Detail Building Custom Dynamic-link Libraries Building a Universal Windows Driver
Linking Quick Start x
Using the /Qmkl Compiler Option Automatically Linking a Project in the Visual Studio* Integrated Development Environment with Intel® oneAPI Math Kernel Library Using the Single Dynamic Library Selecting Libraries to Link with Using the Link-line Advisor Using the Command-Line Link Tool
Automatically Linking a Project in the Visual Studio* Integrated Development Environment with Intel® oneAPI Math Kernel Library x
Automatically Linking Your Microsoft Visual C/C++* Project with oneMKL Automatically Linking Your Intel® Visual Fortran Project with oneMKL
Linking Examples x
Linking on IA-32 Architecture Systems Linking on Intel(R) 64 Architecture Systems
Linking in Detail x
Dynamically Selecting the Interface and Threading Layer Linking with Interface Libraries Linking with Threading Libraries Linking with Computational Libraries Linking with Compiler Run-time Libraries Linking with System Libraries
Linking with Interface Libraries x
Using the ILP64 Interface vs. LP64 Interface Linking with Fortran 95 Interface Libraries
Building Custom Dynamic-link Libraries x
Using the Custom Dynamic-link Library Builder in the Command-line Mode Composing a List of Functions Specifying Function Names Building a Custom Dynamic-link Library in the Visual Studio* Development System Distributing Your Custom Dynamic-link Library
Managing Performance and Memory x
Improving Performance with Threading Improving Performance for Small Size Problems Other Tips and Techniques to Improve Performance Using Memory Functions
Improving Performance with Threading x
OpenMP* Threaded Functions and Problems Functions Threaded with Intel® Threading Building Blocks Avoiding Conflicts in the Execution Environment Techniques to Set the Number of Threads Setting the Number of Threads Using an OpenMP* Environment Variable Changing the Number of OpenMP* Threads at Run Time Using Additional Threading Control Calling oneMKL Functions from Multi-threaded Applications Using Intel® Hyper-Threading Technology Managing Multi-core Performance Managing Performance with Heterogeneous Cores
Using Additional Threading Control x
oneMKL-specific Environment Variables for OpenMP Threading Control MKL_DYNAMIC MKL_DOMAIN_NUM_THREADS <span class='keyword'>MKL_NUM_STRIPES</span> Setting the Environment Variables for Threading Control
Improving Performance for Small Size Problems x
Using MKL_DIRECT_CALL in C Applications Using MKL_DIRECT_CALL in Fortran Applications Limitations of the Direct Call
Other Tips and Techniques to Improve Performance x
Coding Techniques Improving oneMKL Performance on Specific Processors Operating on Denormals
Using Memory Functions x
Avoiding Memory Leaks in oneMKL Redefining Memory Functions
Language-specific Usage Options x
Using Language-Specific Interfaces with Intel® oneAPI Math Kernel Library Mixed-language Programming with the Intel Math Kernel Library
Using Language-Specific Interfaces with Intel® oneAPI Math Kernel Library x
Interface Libraries and Modules Fortran 95 Interfaces to LAPACK and BLAS Compiler-dependent Functions and Fortran 90 Modules
Mixed-language Programming with the Intel Math Kernel Library x
Calling LAPACK, BLAS, and CBLAS Routines from C/C++ Language Environments Using Complex Types in C/C++ Calling BLAS Functions that Return the Complex Values in C/C++ Code
Obtaining Numerically Reproducible Results x
Getting Started with Conditional Numerical Reproducibility Specifying Code Branches Reproducibility Conditions Setting the Environment Variable for Conditional Numerical Reproducibility Code Examples
Coding Tips x
Example of Data Alignment Using Predefined Preprocessor Symbols for Intel® MKL Version-Dependent Compilation
Managing Output x
Using oneMKL Verbose Mode
Using oneMKL Verbose Mode x
Version Information Line Call Description Line
Working with the Intel® oneAPI Math Kernel Library Cluster Software x
Message-Passing Interface Support Linking with oneMKL Cluster Software Determining the Number of OpenMP* Threads Using DLLs Setting Environment Variables on a Cluster Interaction with the Message-passing Interface Using a Custom Message-Passing Interface Examples of Linking for Clusters
Examples of Linking for Clusters x
Examples for Linking a C Application Examples for Linking a Fortran Application
Managing Behavior of the Intel® oneAPI Math Kernel Library with Environment Variables x
Managing Behavior of Function Domains with Environment Variables Instruction Set Specific Dispatching on Intel® Architectures
Managing Behavior of Function Domains with Environment Variables x
Setting the Default Mode of Vector Math with an Environment Variable Managing Performance of the Cluster Fourier Transform Functions Managing Invalid Input Checking in LAPACKE Functions
Programming with Intel® Math Kernel Library in Integrated Development Environments (IDE) x
Configuring Your Integrated Development Environment to Link with Intel® oneAPI Math Kernel Library Getting Assistance for Programming in the Microsoft Visual Studio* IDE
Configuring Your Integrated Development Environment to Link with Intel® oneAPI Math Kernel Library x
Configuring the Microsoft Visual C/C++* Development System to Link with Intel® MKL Configuring Intel® Visual Fortran to Link with Intel MKL
Getting Assistance for Programming in the Microsoft Visual Studio* IDE x
Using Context-Sensitive Help Using the IntelliSense* Capability
Intel® oneAPI Math Kernel Library Benchmarks x
Intel Optimized LINPACK Benchmark for Windows* Intel® Distribution for LINPACK* Benchmark
Intel Optimized LINPACK Benchmark for Windows* x
Contents of the Intel® Optimized LINPACK Benchmark Running the Software Known Limitations of the Intel® Optimized LINPACK Benchmark
Intel® Distribution for LINPACK* Benchmark x
Overview of the Intel® Distribution for LINPACK* Benchmark Contents of the Intel® Distribution for LINPACK* Benchmark Building the Intel® Distribution for LINPACK* Benchmark for a Customized MPI Implementation Building the Netlib HPL from Source Code Configuring Parameters Ease-of-use Command-line Parameters Running the Intel® Distribution for LINPACK* Benchmark Heterogeneous Support in the Intel® Distribution for LINPACK* Benchmark Environment Variables Improving Performance of Your Cluster
Appendix A: Intel® oneAPI Math Kernel Library Language Interfaces Support x
Language Interfaces Support, by Function Domain Include Files
Appendix B: Support for Third-Party Interfaces x
FFTW Interface Support
Appendix C: Directory Structure in Detail x
Detailed Structure of the IA-32 Architecture Directories Detailed Structure of the Intel® 64 Architecture Directories
Detailed Structure of the IA-32 Architecture Directories x
Static Libraries in the <span class='filepath'>lib</span> <span class='filepath'>\\</span> <span class='filepath'>ia32</span> Directory Dynamic Libraries in the <span class='filepath'>lib</span> <span class='filepath'>\\</span> <span class='filepath'>ia32</span> Directory Contents of the <span class='filepath'>redist\\ia32</span> Directory
Detailed Structure of the Intel® 64 Architecture Directories x
Static Libraries in the <span class='filepath'>lib</span> <span class='filepath'>\\</span> <span class='filepath'>intel64</span> Directory Dynamic Libraries in the <span class='filepath'>lib</span> <span class='filepath'>\\</span> <span class='filepath'>intel64</span> Directory Contents of the <span class='filepath'>redist\\intel64</span> Directory
Developer Guide for Intel® oneAPI Math Kernel Library for Windows*

Running the Intel® Distribution for LINPACK* 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 for the Intel MPI Library and Intel® compiler:

    <compiler directory>\env\vars.bat

    <mpi directory>\env\vars.bat

  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.

    NOTE:

    High-bandwidth Multi-Channel Dynamic Random Access Memory (MCDRAM) on the second-generation Intel® Xeon® Phi processors may appear to be a NUMA node. However, because there are no CPUs on this node, do not run an MPI process for it.

    • 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:

      MPI_PROC_NUM
      The total number of MPI processes.
      MPI_PER_NODE
      The number of MPI processes per each cluster node.

    • In the HPL.dat file, set the parameters Ps and Qs so that Ps * Qs equals the number of MPI processes. For example, for 2 processes, set Ps to 1 and Qs to 2. Alternatively, leave the HPL.dat file as is and launch with -p and -q command-line parameters.

  4. Execute runme_intel64_dynamic.bat script:

    runme_intel64_dynamic.bat

  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 www.Intel.com/PerformanceIndex.

Notice revision #20201201

Parent topic: Intel® Distribution for LINPACK* Benchmark

See Also

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