Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Windows*

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ID 766692
Date 10/31/2024
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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® Fortran Project with oneMKL
Linking Examples x
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 See Also 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 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® oneMKL 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
Getting Assistance for Programming in the Microsoft Visual Studio* IDE
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 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 Intel® 64 Architecture Directories
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*

Linking with Threading Libraries

Intel® oneAPI Math Kernel Library (oneMKL) threading layer defines how Intel® oneAPI Math Kernel Library (oneMKL) functions utilize multiple computing cores of the system that the application runs on. You must link your application with one appropriate Intel® oneAPI Math Kernel Library (oneMKL) library in this layer, as explained below. Depending on whether this is a threading or a sequential library, Intel® oneAPI Math Kernel Library (oneMKL) runs in a parallel or sequential mode, respectively.

In the parallel mode, Intel® oneAPI Math Kernel Library (oneMKL) utilizes multiple processor cores available on your system, uses the OpenMP*or Intel TBB threading technology, and requires a proper threading runtime library (RTL) to be linked with your application. Independently of use of Intel® oneAPI Math Kernel Library (oneMKL), the application may also require a threading RTL. You should link not more than one threading RTL to your application. Threading RTLs are provided by your compiler. Intel® oneAPI Math Kernel Library (oneMKL) provides several threading libraries, each dependent on the threading RTL of a certain compiler, and your choice of the Intel® oneAPI Math Kernel Library (oneMKL) threading library must be consistent with the threading RTL that you use in your application.

The OpenMP RTL of the Intel® compiler is the libiomp5md.lib library, located under <parent directory>\compiler\lib. You can find additional information about the Intel OpenMP RTL at https://www.openmprtl.org.

The Intel TBB RTL of the Intel® compiler is the tbb12.lib library, located under <parent directory>\tbb\lib. You can find additional information about the Intel TBB RTL at https://www.threadingbuildingblocks.org.

In the sequential mode, Intel® oneAPI Math Kernel Library (oneMKL) runs unthreaded code, does not require an threading RTL, and does not respond to environment variables and functions controlling the number of threads. Avoid using the library in the sequential mode unless you have a particular reason for that, such as the following:

  • Your application needs a threading RTL that none of Intel® oneAPI Math Kernel Library (oneMKL) threading libraries is compatible with
  • Your application is already threaded at a top level, and using parallel Intel® oneAPI Math Kernel Library (oneMKL) only degrades the application performance by interfering with that threading
  • Your application is intended to be run on a single thread, like a message-passing interface (MPI) application

It is critical to link the application with the proper RTL. The table below explains what library in the Intel® oneAPI Math Kernel Library (oneMKL) threading layer and whatthreading RTL you should choose under different scenarios:

Application

Intel® oneAPI Math Kernel Library (oneMKL)

RTL Required

Uses OpenMP Compiled with

Execution Mode

Threading Layer

no

any compiler

parallel

Static linking:

mkl_intel_thread.lib

Dynamic linking:

mkl_intel_thread_dll.lib

libiomp5md.lib

no

any compiler

parallel

Static linking:

mkl_tbb_thread.lib

Dynamic linking:

mkl_tbb_thread_dll.lib

 tbb12.lib

no

any compiler

sequential

Static linking:

mkl_sequential.lib

Dynamic linking:

mkl_sequential_dll.lib

none

yes

Intel compiler

parallel

Static linking:

mkl_intel_thread.lib

Dynamic linking:

mkl_intel_thread_dll.lib

libiomp5md.lib

yes

any other compiler

parallel

Not supported. Use Intel® oneAPI Math Kernel Library (oneMKL) in the sequential mode.

 

Product and Performance Information

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

Notice revision #20201201

Parent topic: Linking in Detail
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