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
Numerically Reproducible Results for GPU Computations 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*

Obtaining Numerically Reproducible Results

Intel® oneAPI Math Kernel Library (oneMKL) offers functions and environment variables that help you obtain Conditional Numerical Reproducibility (CNR) of floating-point results when calling the library functions from your application. These new controls enable Intel® oneAPI Math Kernel Library (oneMKL) to run in a special mode, when functions return bitwise reproducible floating-point results from run to run under the following conditions:

  • Calls to Intel® oneAPI Math Kernel Library (oneMKL) occur in a single executable
  • The number of computational threads used by the library does not change in the run

For a limited set of routines, you can eliminate the second condition by using Intel® oneAPI Math Kernel Library (oneMKL) in strict CNR mode.

It is well known that for general single and double precision IEEE floating-point numbers, the associative property does not always hold, meaning (a+b)+c may not equal a +(b+c). Let's consider a specific example. In infinite precision arithmetic 2-63 + 1 + -1 = 2-63. If this same computation is done on a computer using double precision floating-point numbers, a rounding error is introduced, and the order of operations becomes important:

(2-63 + 1) + (-1) ≃ 1 + (-1) = 0

versus

2-63 + (1 + (-1)) ≃ 2-63 + 0 = 2-63

This inconsistency in results due to order of operations is precisely what the new functionality addresses.

The application related factors that affect the order of floating-point operations within a single executable program include selection of a code path based on run-time processor dispatching, alignment of data arrays, variation in number of threads, threaded algorithms and internal floating-point control settings. You can control most of these factors by controlling the number of threads and floating-point settings and by taking steps to align memory when it is allocated. However, run-time dispatching and certain threaded algorithms do not allow users to make changes that can ensure the same order of operations from run to run.

Intel® oneAPI Math Kernel Library (oneMKL) does run-time processor dispatching in order to identify the appropriate internal code paths to traverse for the Intel® oneAPI Math Kernel Library (oneMKL) functions called by the application. The code paths chosen may differ across a wide range of Intel processors and Intel architecture compatible processors and may provide differing levels of performance. For example, an Intel® oneAPI Math Kernel Library (oneMKL) function running on an Intel® Pentium® 4 processor may run one code path, while on the latest Intel® Xeon® processor it will run another code path. This happens because each unique code path has been optimized to match the features available on the underlying processor. One key way that the new features of a processor are exposed to the programmer is through the instruction set architecture (ISA). Because of this, code branches in Intel® oneAPI Math Kernel Library (oneMKL) are designated by the latest ISA they use for optimizations: from the Intel® Streaming SIMD Extensions 2 (Intel® SSE2) to the Intel® Advanced Vector Extensions2 (Intel® AVX2). The feature-based approach introduces a challenge: if any of the internal floating-point operations are done in a different order or are re-associated, the computed results may differ.

Dispatching optimized code paths based on the capabilities of the processor on which the code is running is central to the optimization approach used by Intel® oneAPI Math Kernel Library (oneMKL). So it is natural that consistent results require some performance trade-offs. If limited to a particular code path, performance of Intel® oneAPI Math Kernel Library (oneMKL) can in some circumstances degrade by more than a half. To understand this, note that matrix-multiply performance nearly doubled with the introduction of new processors supporting Intel AVX2 instructions. Even if the code branch is not restricted, performance can degrade by 10-20% because the new functionality restricts algorithms to maintain the order of operations.

Numerically Reproducible Results for GPU Computations

oneMKL 2024.1 introduces CNR support for certain GPU computations: level-3 BLAS routines (for example, gemm and trsm) and batched versions of these routines. When any CNR code branch is enabled, GPU CNR support is enabled automatically, ensuring run-to-run bitwise reproducible results for these routines on Intel® GPUs. The specific code branch chosen (AVX2, AVX-512, and so forth) is ignored for GPU execution.

When GPU CNR mode is enabled, oneMKL guarantees that running the same computation multiple times on the same GPU will result in deterministic, bitwise-identical results. Two GPUs are considered to be the same if they have the same product name (for example, Intel® Arc™ A770). Note that results may differ across devices (between CPU and GPU, or between different kinds of GPUs).

Product and Performance Information

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

Notice revision #20201201

Parent topic: Developer Guide for Intel® oneAPI Math Kernel Library for Linux*
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