Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Windows*

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ID 766692
Date 3/31/2025
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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® Math Kernel Library Cluster Edition 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 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 See Also Composing a List of Functions Specifying Function Names 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 Querying oneMATH Specification Version Compliance
Managing Output x
Using oneMKL Verbose Mode
Using oneMKL Verbose Mode x
Version Information Line Call Description Line
Working with the Intel® Math Kernel Library Cluster Edition Software x
Message-Passing Interface Support Linking with Intel® Math Kernel Library Cluster Edition 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*

Using the Custom Dynamic-Link Library Builder in the Command-Line Mode

To build a custom DLL, use the following command:

nmake target [<options>]

The following table lists possible values of target and explains what the command does for each value:

Value

Comment

libintel64

The builder uses static Intel® oneAPI Math Kernel Library (oneMKL) interface, threading, and core libraries to build a custom DLL for the Intel® 64 architecture.

help

The command prints Help on the custom DLL builder

The <options> placeholder stands for the list of parameters that define macros to be used by the makefile. The following table describes these parameters:

Parameter [Values]

Description

interface = {lp64|ilp64}

The lp64 interface uses the 32-bit integer type, while the ilp64 interface uses the 64-bit integer type. The default value is lp64.

threading = {parallel|sequential}

Defines whether to use the Intel® oneAPI Math Kernel Library (oneMKL) in the threaded or sequential mode. The default value is parallel.

parallel = {intel|tbb}

Specifies whether to use Intel OpenMP or Intel® oneTBB. The default value is intel.

cluster = {yes|no}

Specifies whether Intel® oneAPI Math Kernel Library (oneMKL) cluster components (BLACS, ScaLAPACK and/or CDFT) are needed to build the custom shared object. The default value is no.

blacs_mpi = {intelmpi|msmpi}

Specifies the pre-compiled Intel® oneAPI Math Kernel Library (oneMKL) BLACS library to use. Ignored if'cluster=no'. The default value is intelmpi.

All of the above parameters are optional. However, you must make the system and c-runtime (crt) libraries and link.exe available by setting the PATH and LIB environment variables appropriately. You can do this in the following ways:

  • Manually.
  • If you are using Microsoft Visual Studio (VS), call the vcvarsall.bat script with the 64-bit (x64 or amd64) architecture flag.
  • If you are using the Intel compiler, call the setvars.bat script.

In the simplest case, the command line is: 

rem Set environment variables
call <oneAPI_dir>\setvars.bat

rem Run custom dll builder script
nmake libintel64

and the missing options have default values. This command creates the  mkl_custom.dll and mkl_custom.lib libraries with the lp64 interface for processors using the Intel® 64 architecture. The command takes the list of functions from the user_example_list file and uses the native Intel® oneAPI Math Kernel Library (oneMKL) error handlerxerbla.

Here is an example of a more complex case: 

rem Set Visual Studio environment variables
call <script_dir>\vcvarsall.bat x64

rem Run custom dll builder script
nmake libintel64 threading=sequential export=my_func_list name=mkl_small xerbla=my_xerbla.obj

In this case, the command creates the mkl_small.dll and mkl_small.lib libraries with oneMKL sequential mode for processors using the Intel® 64 architecture. The command takes the list of functions from the my_func_list file and uses the error handler of the user my_xerbla.obj.

To build a UWD-compatible custom dll, use the uwd_compat=yes option. For this purpose, you must make a different set of universal system (OneCore.lib) and universal c-runtime (ucrt.lib) libraries available. You can get these libraries by downloading Windows 10 SDK 10.0.17134.0 (version 1803) or newer. Make sure to source the Visual Studio environment with the appropriate native architecture to add the libraries to your path.

This example shows how to create a 64-bit architecture library, mkl_uwd_compat.dll, that is UWD-compatible with the lp64 interface using my_func_list for specific functionality: 

rem Set Visual Studio environment variables. LIB should have paths to desired OneCore.lib and universal crt libraries.
call <script_dir>\vcvarsall.bat x64

rem Run custom dll builder script
nmake libintel64 interface=lp64 export=my_func_list uwd_compat=yes name=mkl_uwd_compat

Product and Performance Information

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

Notice revision #20201201

Parent topic: Building Custom Dynamic-link Libraries
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