Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Linux*

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ID 766690
Date 12/16/2022
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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 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 LAPACK Routines Threaded BLAS Level1 and Level2 Routines Threaded FFT 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 <span class='keyword'>MKL_NUM_STRIPES</span> 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 Using High-bandwidth Memory with 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 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 x
Overview of the Intel® Distribution for LINPACK* Benchmark Contents of the Intel® Distribution for LINPACK* Benchmark Building the Intel® Distribution for LINPACK* 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 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 Optimized HPCG Getting Started with Intel Optimized HPCG Choosing Best Configuration and Problem Sizes
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 <span class='filepath'>lib</span> <span class='filepath'>/</span> <span class='filepath'>ia32</span> <span class='filepath'>_lin</span> Directory Dynamic Libraries in the <span class='filepath'>lib</span> <span class='filepath'>/</span> <span class='filepath'>ia32</span> <span class='filepath'>_lin</span> Directory
Detailed Structure of the Intel® 64 Architecture Directories x
Static Libraries in the <span class='filepath'>lib</span> <span class='filepath'>/</span> <span class='filepath'>intel64</span> <span class='filepath'>_lin</span> Directory Dynamic Libraries in the <span class='filepath'>lib</span> <span class='filepath'>/</span> <span class='filepath'>intel64</span> <span class='filepath'>_lin</span> Directory
Developer Guide for Intel® oneAPI Math Kernel Library for Linux*

OpenMP* Threaded Functions and Problems

The following Intel® oneAPI Math Kernel Library function domains are threaded with the OpenMP* technology:

  • Direct sparse solver.

  • LAPACK.

    For a list of threaded routines, see LAPACK Routines.

  • Level1 and Level2 BLAS.

    For a list of threaded routines, see BLAS Level1 and Level2 Routines.

  • All Level 3 BLAS and all Sparse BLAS routines except Level 2 Sparse Triangular solvers.

  • All Vector Mathematics functions (except service functions).

  • FFT.

    For a list of FFT transforms that can be threaded, see Threaded FFT Problems.

Product and Performance Information

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

Notice revision #20201201

LAPACK Routines

In this section, ? stands for a precision prefix of each flavor of the respective routine and may have the value of s, d, c, or z.

The following LAPACK routines are threaded with OpenMP*:

  • Linear equations, computational routines:
    • Factorization: ?getrf, ?getrfnpi, ?gbtrf, ?potrf, ?pptrf, ?sytrf, ?hetrf, ?sptrf, ?hptrf
    • Solving: ?dttrsb, ?gbtrs, ?gttrs, ?pptrs, ?pbtrs, ?pttrs, ?sytrs, ?sptrs, ?hptrs, ?tptrs, ?tbtrs
  • Orthogonal factorization, computational routines:
    ?geqrf, ?ormqr, ?unmqr, ?ormlq, ?unmlq, ?ormql, ?unmql, ?ormrq, ?unmrq
  • Singular Value Decomposition, computational routines:
    ?gebrd, ?bdsqr
  • Symmetric Eigenvalue Problems, computational routines:
    ?sytrd, ?hetrd, ?sptrd, ?hptrd, ?steqr, ?stedc    .
  • Generalized Nonsymmetric Eigenvalue Problems, computational routines:
    chgeqz/zhgeqz    .

A number of other LAPACK routines, which are based on threaded LAPACK or BLAS routines, make effective use of OpenMP* parallelism:
?gesv, ?posv, ?gels, ?gesvd, ?syev, ?heev, cgegs/zgegs, cgegv/zgegv, cgges/zgges, cggesx/zggesx, cggev/zggev, cggevx/zggevx, and so on.

Threaded BLAS Level1 and Level2 Routines

In the following list, ? stands for a precision prefix of each flavor of the respective routine and may have the value of s, d, c, or z.

The following routines are threaded with OpenMP*:

  • Level1 BLAS:
    ?axpy, ?copy, ?swap, ddot/sdot, cdotc, drot/srot
  • Level2 BLAS:
    ?gemv, ?trsv, ?trmv, dsyr/ssyr, dsyr2/ssyr2, dsymv/ssymv

Threaded FFT Problems

The following characteristics of a specific problem determine whether your FFT computation may be threaded with OpenMP*:

  • rank
  • domain
  • size/length
  • precision (single or double)
  • placement (in-place or out-of-place)
  • strides
  • number of transforms
  • layout (for example, interleaved or split layout of complex data)

Most FFT problems are threaded. In particular, computation of multiple transforms in one call (number of transforms > 1) is threaded. Details of which transforms are threaded follow.

One-dimensional (1D) transforms

1D transforms are threaded in many cases.

1D complex-to-complex (c2c) transforms of size N using interleaved complex data layout are threaded under the following conditions depending on the architecture:

Architecture

Conditions

Intel® 64

N is a power of 2, log2(N) > 9, the transform is double-precision out-of-place, and input/output strides equal 1.

IA-32

N is a power of 2, log2(N) > 13, and the transform is single-precision.

N is a power of 2, log2(N) > 14, and the transform is double-precision.

Any

N is composite, log2(N) > 16, and input/output strides equal 1.

1D complex-to-complex transforms using split-complex layout are not threaded.

Multidimensional transforms

All multidimensional transforms on large-volume data are threaded.

Parent topic: Improving Performance with Threading
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