Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library macOS*

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ID 766688
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 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 Intel CPUs supporting Intel AVX2 Intel CPUs supporting Intel SSE2 Intel or Intel compatible CPUs supporting Intel SSE2 Next steps See Also 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 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*

Getting Started with Conditional Numerical Reproducibility

Intel® oneAPI Math Kernel Library (oneMKL) offers functions and environment variables to help you get reproducible results. You can configure Intel® oneAPI Math Kernel Library (oneMKL) using functions or environment variables, but the functions provide more flexibility.

The following specific examples introduce you to the conditional numerical reproducibility.

While these examples recommend aligning input and output data, you can supply unaligned data to Intel® oneAPI Math Kernel Library (oneMKL) functions running in the CNR mode, but refer toReproducibility Conditions for details related to data alignment.

Intel CPUs supporting Intel AVX2

To ensure Intel® oneAPI Math Kernel Library (oneMKL) calls return the same results on every Intel CPU supporting Intel AVX2 instructions:

  1. Make sure that your application uses a fixed number of threads

  2. (Recommended) Properly align input and output arrays in Intel® oneAPI Math Kernel Library (oneMKL) function calls

  3. Do either of the following:

    • Call

      mkl_cbwr_set(MKL_CBWR_AVX2)

    • Set the environment variable:

      export MKL_CBWR = AVX2

NOTE:

On non-Intel CPUs and on Intel CPUs that do not support Intel AVX2, this environment setting may cause results to differ because the AUTO branch is used instead, while the above function call returns an error and does not enable the CNR mode.

Intel CPUs supporting Intel SSE2

To ensure Intel® oneAPI Math Kernel Library (oneMKL) calls return the same results on every Intel CPU supporting Intel SSE2instructions:

  1. Make sure that your application uses a fixed number of threads

  2. (Recommended) Properly align input and output arrays in Intel® oneAPI Math Kernel Library (oneMKL) function calls

  3. Do either of the following:

    • Call

      mkl_cbwr_set(MKL_CBWR_SSE2)

    • Set the environment variable:

      export MKL_CBWR = SSE2

NOTE:

On non-Intel CPUs, this environment setting may cause results to differ because the AUTO branch is used instead, while the above function call returns an error and does not enable the CNR mode.

Intel or Intel compatible CPUs supporting Intel SSE2

On non-Intel CPUs, only the MKL_CBWR_AUTO and MKL_CBWR_COMPATIBLE options are supported for function calls and only AUTO and COMPATIBLE options for environment settings.

To ensure Intel® oneAPI Math Kernel Library (oneMKL) calls return the same results on all Intel or Intel compatible CPUs supporting Intel SSE2 instructions:

  1. Make sure that your application uses a fixed number of threads

  2. (Recommended) Properly align input and output arrays in Intel® oneAPI Math Kernel Library (oneMKL) function calls

  3. Do either of the following:

    • Call

      mkl_cbwr_set(MKL_CBWR_COMPATIBLE)

    • Set the environment variable:

      export MKL_CBWR = COMPATIBLE

NOTE:

The special MKL_CBWR_COMPATIBLE/COMPATIBLEoption is provided because Intel and Intel compatible CPUs have a few instructions, such as approximation instructions rcpps/rsqrtps, that may return different results. This option ensures that Intel® oneAPI Math Kernel Library (oneMKL) does not use these instructions and forces a single Intel SSE2 only code path to be executed.

Next steps

See Specifying the Code Branches

for details of specifying the branch using environment variables.

See the following sections in the Intel® oneAPI Math Kernel Library (oneMKL) Developer Reference:

Support Functions for Conditional Numerical Reproducibility

for how to configure the CNR mode of Intel® oneAPI Math Kernel Library (oneMKL) using functions.

Intel® oneAPI Math Kernel Library (oneMKL) PARDISO - Parallel Direct Sparse Solver Interface

for how to configure the CNR mode for PARDISO.

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

See Also

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