Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Linux*

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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 Linux*
Developer Guide for Intel® oneAPI Math Kernel Library for Linux* 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 Configuring Your Integrated Development Environment to Link with Intel® oneAPI Math Kernel Library 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
Setting Environment Variables x
Scripts to Set Environment Variables Modulefiles to Set Environment Variables Automating the Process of Setting Environment Variables Using the CMake Config File
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 Shared Objects
Linking Quick Start x
Using the -qmkl Compiler Option Using the -qmkl-ilp64 Compiler Option Using the Single Dynamic Library Selecting Libraries to Link with Using the Link-line Advisor Using the Command-Line Link Tool
Linking Examples x
Linking on IA-32 Architecture Systems Linking on Intel(R) 64 Architecture Systems
Linking in Detail x
Listing Libraries on a Link Line 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 Shared Objects x
Using the Custom Shared Object Builder Composing a List of Functions Specifying Function Names Distributing Your Custom Shared Object
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 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
Linking with oneMKL Cluster Software Setting the Number of OpenMP* Threads Using Shared Libraries 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
Configuring Your Integrated Development Environment to Link with Intel® oneAPI Math Kernel Library x
Configuring the Eclipse* IDE CDT to Link with oneMKL
Intel® oneAPI Math Kernel Library Benchmarks x
Intel Optimized LINPACK Benchmark for Linux* Intel® Distribution for LINPACK* Benchmark and Intel® Optimized HPL-AI* Benchmark Intel® Optimized High Performance Conjugate Gradient Benchmark
Intel Optimized LINPACK Benchmark for Linux* 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
Intel® Optimized High Performance Conjugate Gradient Benchmark x
Overview of the Intel Optimized HPCG Versions of the Intel® CPU Optimized HPCG Versions of the Intel® GPU Optimized HPCG Getting Started with Intel® CPU Optimized HPCG Getting Started with Intel® GPU Optimized HPCG Choosing the Best Configuration and Problem Sizes for CPUs Choosing the Best HPCG Configuration for GPUs
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
Detailed Structure of the Intel® 64 Architecture Directories x
Static Libraries in the lib Directory Dynamic Libraries in the lib Directory
Developer Guide for Intel® oneAPI Math Kernel Library for Linux*

What You Need to Know Before You Begin Using the Intel® oneAPI Math Kernel Library

Target platform

Identify the architecture of your target machine:

  • IA-32 or compatible
  • Intel® 64 or compatible

Reason:Because Intel® oneAPI Math Kernel Library (oneMKL) libraries are located in directories corresponding to your particular architecture (seeArchitecture Support), you should provide proper paths on your link lines (see Linking Examples).To configure your development environment for the use with Intel® oneAPI Math Kernel Library (oneMKL), set your environment variables using the script corresponding to your architecture (see Scripts to Set Environment Variables Setting Environment Variables for details).

Mathematical problem

Identify all Intel® oneAPI Math Kernel Library (oneMKL) function domains that you require:

  • BLAS
  • Sparse BLAS
  • LAPACK
  • PBLAS
  • ScaLAPACK
  • Sparse Solver routines
  • Parallel Direct Sparse Solvers for Clusters
  • Vector Mathematics functions (VM)
  • Vector Statistics functions (VS)
  • Fourier Transform functions (FFT)
  • Cluster FFT
  • Trigonometric Transform routines
  • Poisson, Laplace, and Helmholtz Solver routines
  • Optimization (Trust-Region) Solver routines
  • Data Fitting Functions
  • Extended Eigensolver Functions

Reason: The function domain you intend to use narrows the search in the Intel® oneAPI Math Kernel Library (oneMKL) Developer Referencefor specific routines you need. Additionally, if you are using the Intel® oneAPI Math Kernel Library (oneMKL) cluster software, your link line is function-domain specific (seeWorking with the Intel® oneAPI Math Kernel Library Cluster Software). Coding tips may also depend on the function domain (see Other Tips and Techniques to Improve Performance).

Programming language

Intel® oneAPI Math Kernel Library (oneMKL) provides support for both Fortran and C/C++ programming. Identify the language interfaces that your function domains support (see Appendix A: Intel® oneAPI Math Kernel Library Language Interfaces Support).

Reason:Intel® oneAPI Math Kernel Library (oneMKL) provides language-specific include files for each function domain to simplify program development (seeLanguage Interfaces Support_ by Function Domain).

For a list of language-specific interface libraries and modules and an example how to generate them, see also Using Language-Specific Interfaces with Intel® oneAPI Math Kernel Library.

Range of integer data

If your system is based on the Intel 64 architecture, identify whether your application performs calculations with large data arrays (of more than 231-1 elements).

Reason: To operate on large data arrays, you need to select the ILP64 interface, where integers are 64-bit; otherwise, use the default, LP64, interface, where integers are 32-bit (see Using the ILP64 Interface vs).

Threading model

Identify whether and how your application is threaded:

  • Threaded with the Intel compiler
  • Threaded with a third-party compiler
  • Not threaded

Reason:The compiler you use to thread your application determines which threading library you should link with your application. For applications threaded with a third-party compiler you may need to use Intel® oneAPI Math Kernel Library (oneMKL) in the sequential mode (for more information, seeLinking with Threading Libraries).

Number of threads

If your application uses an OpenMP* threading run-time library, determine the number of threads you want Intel® oneAPI Math Kernel Library (oneMKL) to use.

Reason:By default, the OpenMP* run-time library sets the number of threads for Intel® oneAPI Math Kernel Library (oneMKL). If you need a different number, you have to set it yourself using one of the available mechanisms. For more information, seeImproving Performance with Threading.

Linking model

Decide which linking model is appropriate for linking your application with Intel® oneAPI Math Kernel Library (oneMKL) libraries:

  • Static
  • Dynamic

Reason: The link line syntax and libraries for static and dynamic linking are different. For the list of link libraries for static and dynamic models, linking examples, and other relevant topics, like how to save disk space by creating a custom dynamic library, see Linking Your Application with the Intel® oneAPI Math Kernel Library.

MPI used

Decide what MPI you will use with the Intel® oneAPI Math Kernel Library (oneMKL) cluster software. You are strongly encouraged to use the latest available version of Intel® MPI.

Reason: To link your application with ScaLAPACK and/or Cluster FFT, the libraries corresponding to your particular MPI should be listed on the link line (see Working with the Intel® oneAPI Math Kernel Library Cluster Software).

Product and Performance Information

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

Notice revision #20201201

Parent topic: Getting Started
Level Two Title