Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Windows*

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ID 766692
Date 11/07/2023
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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 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 Example "Calling a Complex BLAS Level 1 Function from C" Example "Calling a Complex BLAS Level 1 Function from C++" Example "Using CBLAS Interface Instead of Calling BLAS Directly from C"
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*

Calling BLAS Functions That Return the Complex Values in C/C++ Code

Complex values that functions return are handled differently in C and Fortran. Because BLAS is Fortran-style, you need to be careful when handling a call from C to a BLAS function that returns complex values. However, in addition to normal function calls, Fortran enables calling functions as though they were subroutines, which provides a mechanism for returning the complex value correctly when the function is called from a C program. When a Fortran function is called as a subroutine, the return value is the first parameter in the calling sequence. You can use this feature to call a BLAS function from C.

The following example shows how a call to a Fortran function as a subroutine converts to a call from C and the hidden parameter result gets exposed:

Normal Fortran function call:                   result = cdotc( n, x, 1, y, 1 )

A call to the function as a subroutine:  call cdotc( result, n, x, 1, y, 1)

A call to the function from C:                  cdotc( &result, &n, x, &one, y, &one )

NOTE:

Intel® oneAPI Math Kernel Library (oneMKL) has upper-case, lower-case, and lower-case with the trailing underscore entry points in the Fortran-style (case-insensitive) BLAS. So, all these names are equivalent and acceptable:cdotc, cdotc_, and CDOTC.

The above example shows one of the ways to call several level 1 BLAS functions that return complex values from your C and C++ applications. An easier way is to use the CBLAS interface. For instance, you can call the same function using the CBLAS interface as follows:

cblas_cdotc( n, x, 1, y, 1, &result )

NOTE:

The complex value comes last on the argument list in this case.

The following examples show use of the Fortran-style BLAS interface from C and C++, as well as the CBLAS (C language) interface:

Example "Calling a Complex BLAS Level 1 Function from C"

The example below illustrates a call from a C program to the complex BLAS Level 1 function zdotc(). This function computes the dot product of two double-precision complex vectors.

In this example, the complex dot product is returned in the structure c. 

                         
#include "mkl.h"
#define N 5
int main()
{
int n = N, inca = 1, incb = 1, i;
MKL_Complex16 a[N], b[N], c;
for( i = 0; i < n; i++ )
{
 a[i].real = (double)i; a[i].imag = (double)i * 2.0;
 b[i].real = (double)(n - i); b[i].imag = (double)i * 2.0;
}
zdotc( &c, &n, a, &inca, b, &incb );
printf( "The complex dot product is: ( %6.2f, %6.2f)\n", c.real, c.imag );
return 0;
}

In this example, the complex dot product for large data size is returned in the structure c. 

#include "mkl.h"
 #define N 5
 int main()
 {
     MKL_INT64 n = N, inca = 1, incb = 1, i;
     MKL_Complex16 a[N], b[N], c;
     for( i = 0; i < n; i++ )
     {
         a[i].real = (double)i; a[i].imag = (double)i * 2.0;
         b[i].real = (double)(n - i); b[i].imag = (double)i * 2.0;
     }
     zdotc_64( &c, &n, a, &inca, b, &incb );
     printf( "The complex dot product is: ( %6.2f, %6.2f)\n", c.real, c.imag );
     return 0;
 }

Example "Calling a Complex BLAS Level 1 Function from C++"

Below is the C++ implementation: 

                         
#include <complex>
#include <iostream>
#define MKL_Complex16 std::complex<double>
#include "mkl.h"

#define N 5

int main()
{
    int n, inca = 1, incb = 1, i;
    std::complex<double> a[N], b[N], c;
    n = N;
    
    for( i = 0; i < n; i++ )
 {
        a[i] = std::complex<double>(i,i*2.0);
        b[i] = std::complex<double>(n-i,i*2.0);
    }
    zdotc(&c, &n, a, &inca, b, &incb );
    std::cout << "The complex dot product is: " << c << std::endl;
    return 0;
}

Example "Using CBLAS Interface Instead of Calling BLAS Directly from C"

This example uses CBLAS: 

                  
#include <stdio.h>
#include "mkl.h"
typedef struct{ double re; double im; } complex16;
#define N 5
int main()
{
int n, inca = 1, incb = 1, i;
complex16 a[N], b[N], c;
n = N;
for( i = 0; i < n; i++ )
{
 a[i].re = (double)i; a[i].im = (double)i * 2.0;
 b[i].re = (double)(n - i); b[i].im = (double)i * 2.0;
}
cblas_zdotc_sub(n, a, inca, b, incb, &c );
printf( "The complex dot product is: ( %6.2f, %6.2f)\n", c.re, c.im );
return 0;
}
Parent topic: Mixed-language Programming with the Intel Math Kernel Library
Level Two Title