Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library macOS*

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ID 766688
Date 7/13/2023
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Document Table of Contents
Document Table of Contents x
Developer Guide for Intel® oneAPI Math Kernel Library for macOS*
Developer Guide for Intel® oneAPI Math Kernel Library for macOS* 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
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 Dynamically Linked Shared Libraries
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 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 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 Dynamically Linked Shared Libraries x
Using the Custom Dynamically Linked Shared Library Builder Composing a List of Functions Specifying Function Names Distributing Your Custom Dynamically Linked Shared 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
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 See Also 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 Apple Xcode* Developer Software to Link with Intel® Math Kernel Library
Intel® oneAPI Math Kernel Library Benchmarks x
Intel Optimized LINPACK Benchmark for macOS*
Intel Optimized LINPACK Benchmark for macOS* x
Contents of the Intel® Optimized LINPACK Benchmark Running the Software Known Limitations of the Intel® Optimized LINPACK Benchmark
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
Static Libraries in the lib directory Dynamic Libraries in the lib directory
Developer Guide for Intel® oneAPI Math Kernel Library for macOS*

Specifying Code Branches

Intel® oneAPI Math Kernel Library (oneMKL) provides a conditional numerical reproducibility (CNR) functionality that enables you to obtain reproducible results from oneMKL routines. When enabling CNR, you choose a specific code branch of Intel® oneAPI Math Kernel Library (oneMKL) that corresponds to the instruction set architecture (ISA) that you target. You can specify the code branch and other CNR options using theMKL_CBWR environment variable.

  • MKL_CBWR="<branch>[,STRICT]" or

  • MKL_CBWR="BRANCH=<branch>[,STRICT]"

Use the STRICT flag to enable strict CNR mode. For more information, see Reproducibility Conditions.

The <branch> placeholder specifies the CNR branch with one of the following values:

Value

Description

AUTO

CNR mode uses the standard ISA-based dispatching model while ensuring fixed cache sizes, deterministic reductions, and static scheduling

COMPATIBLE

Intel® Streaming SIMD Extensions 2 (Intel® SSE2) without rcpps/rsqrtps instructions

SSE4_2

Intel® Streaming SIMD Extensions 4.2 (Intel® SSE4.2)

AVX

Intel® Advanced Vector Extensions (Intel® AVX)

AVX2

Intel® Advanced Vector Extensions 2 (Intel® AVX2)

AVX512

Intel AVX-512 on Intel® Xeon® processors

AVX512_E1

Intel® Advanced Vector Extensions 512 (Intel® AVX-512) with support for Vector Neural Network Instructions

AVX512_MIC

DEPRECATED. Intel® Advanced Vector Extensions 512 (Intel® AVX-512) on Intel® Xeon Phi™ processors. This setting is kept for backward compatibility and is equivalent to AVX2.

AVX512_MIC_E1

DEPRECATED. Intel® Advanced Vector Extensions 512 (Intel® AVX-512) with support for Vector Neural Network Instructions on Intel® Xeon Phi™ processors. This setting is kept for backward compatibility and is equivalent to AVX2.

When specifying the CNR branch, be aware of the following:

  • Reproducible results are provided under Reproducibility Conditions.

  • Settings other than AUTO or COMPATIBLE are available only for Intel processors.

  • To get the CNR branch optimized for the processor where your program is currently running, choose the value of AUTO or call the mkl_cbwr_get_auto_branch function.

  • Strict CNR mode is supported only for AVX2, AVX512, AVX512_E1, AVX512_MIC, and AVX512_MIC_E1 branches. You can also use strict CNR mode with the AUTO branch when running on Intel processors that support one of these instruction set architectures (ISAs).

Setting the MKL_CBWR environment variable or a call to an equivalent mkl_cbwr_set function fixes the code branch and sets the reproducibility mode.

NOTE:

  • If the value of the branch is incorrect or your processor or operating system does not support the specified ISA, CNR ignores this value and uses the AUTO branch without providing any warning messages.

  • Calls to functions that define the behavior of CNR must precede any of the math library functions that they control.

  • Settings specified by the functions take precedence over the settings specified by the environment variable.

See the Intel® oneAPI Math Kernel Library (oneMKL) Developer Reference for how to specify the branches using functions.

Product and Performance Information

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

Notice revision #20201201

Parent topic: Obtaining Numerically Reproducible Results
Level Two Title