Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Windows*

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Date 3/22/2024
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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 Using the /Qmkl-ilp64 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 MKL_NUM_STRIPES 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 Data Alignment and Leading Dimensions LAPACK Packed Routines See Also 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 oneMKL
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 and Intel® Optimized HPL-AI* 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 and Intel® Optimized HPL-AI* Benchmark x
Overview of the Intel® Distribution for LINPACK* Benchmark Overview of the Intel® Optimized HPL-AI* Benchmark Contents of the Intel® Distribution for LINPACK* Benchmark and the Intel® Optimized HPL-AI* Benchmark Building the Intel® Distribution for LINPACK* Benchmark and the Intel® Optimized HPL-AI* 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 and the Intel® Optimized HPL-AI* 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 lib32 Directory Dynamic Libraries in the lib32 Directory Contents of the bin32 Directory
Detailed Structure of the Intel® 64 Architecture Directories x
Static Libraries in the lib Directory Dynamic Libraries in the lib Directory Contents of the bin Directory
Developer Guide for Intel® oneAPI Math Kernel Library for Windows*

Coding Techniques

To improve performance, properly align arrays in your code. For more details, see Example of Data Alignment. Additional conditions can improve performance for specific function domains.

Data Alignment and Leading Dimensions

To improve performance of your application that calls Intel® oneAPI Math Kernel Library (oneMKL), align your arrays on 64-byte boundaries and pad the leading dimensions, keeping in mind the following recommendations:

Recommendation 1:
For best performance, the leading dimensions of matrices must be a multiple of 64 bytes (full cache line size). For single-precision real data, this means that the leading dimension must be a multiple of (64 / sizeof(float)), which is equal to 16 elements. Similarly, for double-precision real data or single-precision complex data, the leading dimension must be a multiple of 8 elements, while for double-precision complex data, the leading dimension should be a multiple of 4 elements.
Recommendation 2:
For best performance, leading dimensions must not be a multiple of a large power of 2 (for example, 4096 bytes). Increasing the leading dimension slightly (for example, from 4096 bytes to 4096+64 bytes) can improve performance in some cases.

LAPACK Packed Routines

The routines with the names that contain the letters HP, OP, PP, SP, TP, UP in the matrix type and storage position (the second and third letters respectively) operate on the matrices in the packed format (see LAPACK "Routine Naming Conventions" sections in the Intel® oneAPI Math Kernel Library (oneMKL) Developer Reference). Their functionality is strictly equivalent to the functionality of the unpacked routines with the names containing the lettersHE, OR, PO, SY, TR, UN in the same positions, but the performance is significantly lower.

If the memory restriction is not too tight, use an unpacked routine for better performance. In this case, you need to allocate N2/2 more memory than the memory required by a respective packed routine, where N is the problem size (the number of equations).

For example, to speed up solving a symmetric eigenproblem with an expert driver, use the unpacked routine: 

            
call dsyevx(jobz, range, uplo, n, a, lda, vl, vu, il, iu, abstol, m, w, z, ldz, work, lwork, iwork, ifail, info)

where a is the dimension lda-by-n, which is at least N2 elements,
instead of the packed routine: 


call dspevx(jobz, range, uplo, n, ap, vl, vu, il, iu, abstol, m, w, z, ldz, work, iwork, ifail, info)

where ap is the dimension N*(N+1)/2.

Product and Performance Information

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.

Notice revision #20201201

Parent topic: Other Tips and Techniques to Improve Performance
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