Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Linux*

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ID 766690
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*

Managing Multi-core Performance

You can obtain best performance on systems with multi-core processors by requiring thatthreads do not migrate from core to core. To do this, bind threads to the CPU cores bysetting an affinity mask to threads. Use one of the following options:

  • OpenMP facilities (if available), for example, theKMP_AFFINITYenvironment variable using the Intel OpenMP library
  • A system function, as explained below
  • Intel TBB facilities (if available), for example, the tbb::affinity_partitioner class (for details, see https://www.threadingbuildingblocks.org/documentation)

Consider the following performance issue:

  • The system has two sockets with two cores each, for a total of four cores (CPUs).
  • The application sets the number of OpenMP threads to two and calls Intel® oneAPI Math Kernel Library (oneMKL) to perform a Fourier transform. This call takes considerably different amounts of time from run to run.

To resolve this issue, before calling Intel® oneAPI Math Kernel Library (oneMKL), set an affinity mask for each OpenMP thread using theKMP_AFFINITY environment variable or the sched_setaffinity system function. The following code example shows how to resolve the issue by setting an affinity mask by operating system means using the Intel compiler. The code calls the functionsched_setaffinityto bind the threads tothecoreson different sockets. Then the Intel® oneAPI Math Kernel Library (oneMKL)FFT functionis called:

        
#define _GNU_SOURCE //for using the GNU CPU affinity
// (works with the appropriate kernel and glibc)
// Set affinity mask
#include <sched.h>
#include <stdio.h>
#include <unistd.h>
#include <omp.h>
int main(void) {
	int NCPUs = sysconf(_SC_NPROCESSORS_CONF);
	printf("Using thread affinity on %i NCPUs\n", NCPUs);
#pragma omp parallel default(shared)
	{
		cpu_set_t new_mask;
		cpu_set_t was_mask;
		int tid = omp_get_thread_num();
		
		CPU_ZERO(&new_mask);
		
		// 2 packages x 2 cores/pkg x 1 threads/core (4 total cores)
		CPU_SET(tid==0 ? 0 : 2, &new_mask);
		
		if (sched_getaffinity(0, sizeof(was_mask), &was_mask) == -1) {
			printf("Error: sched_getaffinity(%d, sizeof(was_mask), &was_mask)\n", tid);
		}
		if (sched_setaffinity(0, sizeof(new_mask), &new_mask) == -1) {
			printf("Error: sched_setaffinity(%d, sizeof(new_mask), &new_mask)\n", tid);
		}
		printf("tid=%d new_mask=%08X was_mask=%08X\n", tid,
						*(unsigned int*)(&new_mask), *(unsigned int*)(&was_mask));
	}
	// Call Intel MKL FFT function
	return 0;
}
 

Compile the application with the Intel compiler using the following command:

icx test_application.c -openmp 

wheretest_application.cis the filename for the application.

Build the application. Run it in two threads, for example, by using the environment variable to set the number of threads:

env OMP_NUM_THREADS=2 ./a.out

See the Linux Programmer's Manual (in man pages format) for particulars of the sched_setaffinityfunction used in the above example.

Product and Performance Information

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

Notice revision #20201201

Parent topic: Improving Performance with Threading
Level Two Title