Developer Guide

Developer Guide for Intel® oneAPI Math Kernel Library Windows*

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ID 766692
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 Windows*
Developer Guide for Intel® oneAPI Math Kernel Library for Windows* 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 Programming with Intel® Math Kernel Library in Integrated Development Environments (IDE) 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 Dynamic-link Libraries Building a Universal Windows Driver
Linking Quick Start x
Using the /Qmkl Compiler Option Automatically Linking a Project in the Visual Studio* Integrated Development Environment with Intel® oneAPI Math Kernel Library Using the Single Dynamic Library Selecting Libraries to Link with Using the Link-line Advisor Using the Command-Line Link Tool
Automatically Linking a Project in the Visual Studio* Integrated Development Environment with Intel® oneAPI Math Kernel Library x
Automatically Linking Your Microsoft Visual C/C++* Project with oneMKL Automatically Linking Your Intel® Visual Fortran Project with oneMKL
Linking Examples x
Linking on IA-32 Architecture Systems Linking on Intel(R) 64 Architecture Systems
Linking in Detail x
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 Dynamic-link Libraries x
Using the Custom Dynamic-link Library Builder in the Command-line Mode Composing a List of Functions Specifying Function Names Building a Custom Dynamic-link Library in the Visual Studio* Development System Distributing Your Custom Dynamic-link 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 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 Verbose with CPU Applications Verbose with GPU Applications For Both CPU and GPU Verbose See Also
Using oneMKL Verbose Mode x
Verbose with CPU Applications Verbose with GPU Applications For Both CPU and GPU Verbose See Also Version Information Line Call Description Line
Working with the Intel® oneAPI Math Kernel Library Cluster Software x
Message-Passing Interface Support Linking with oneMKL Cluster Software Determining the Number of OpenMP* Threads Using DLLs 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
Programming with Intel® Math Kernel Library in Integrated Development Environments (IDE) x
Configuring Your Integrated Development Environment to Link with Intel® oneAPI Math Kernel Library Getting Assistance for Programming in the Microsoft Visual Studio* IDE
Configuring Your Integrated Development Environment to Link with Intel® oneAPI Math Kernel Library x
Configuring the Microsoft Visual C/C++* Development System to Link with Intel® MKL Configuring Intel® Visual Fortran to Link with Intel MKL
Getting Assistance for Programming in the Microsoft Visual Studio* IDE x
Using Context-Sensitive Help Using the IntelliSense* Capability
Intel® oneAPI Math Kernel Library Benchmarks x
Intel Optimized LINPACK Benchmark for Windows* Intel® Distribution for LINPACK* Benchmark and Intel® Optimized HPL-AI* Benchmark
Intel Optimized LINPACK Benchmark for Windows* 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
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 lib\\ia32 Directory Dynamic Libraries in the lib\\ia32 Directory Contents of the redist\\ia32 Directory
Detailed Structure of the Intel® 64 Architecture Directories x
Static Libraries in the lib\\intel64 Directory Dynamic Libraries in the lib\\intel64 Directory Contents of the redist\\intel64 Directory
Developer Guide for Intel® oneAPI Math Kernel Library for Windows*

Using oneMKL Verbose Mode

When building applications that call Intel® oneAPI Math Kernel Library (oneMKL) functions, it may be useful to determine:

  • which computational functions are called
  • what parameters are passed to them
  • how much time is spent to execute the functions
  • (for GPU applications) which GPU device the kernel is executed on

You can get an application to print this information to a standard output device by enabling Intel® oneAPI Math Kernel Library (oneMKL) Verbose. Functions that can print this information are referred to as verbose-enabled functions.

When Verbose mode is active in an Intel® oneAPI Math Kernel Library (oneMKL) domain, every call of a verbose-enabled function finishes with printing a human-readable line describing the call. However, if your application gets terminated for some reason during the function call, no information for that function will be printed. The first call to a verbose-enabled function also prints a version information line.

For GPU applications, additional information (one or more GPU information lines) will also be printed by the first call to a verbose-enabled function, following the version information line which will be printed for the host CPU. If there is more than one GPU detected, each GPU device will be printed in a separate line.

We have different implementations for verbose with CPU applications and verbose with GPU applications. The Intel® MKL Verbose mode has 2 modes when used with CPU applications: disabled (default) and enabled. The Intel® MKL Verbose mode has three modes when used with GPU applications: disabled (default), enabled without timing, and enabled with synchronous timing.

To change the verbose mode, either set the environment variable MKL_VERBOSE:

  CPU application GPU application
Set MKL_VERBOSE to 0 to disable Verbose to disable Verbose
Set MKL_VERBOSE to 1 to enable Verbose to enable Verbose without timing
Set MKL_VERBOSE to 2 to enable Verbose to enable Verbose with synchronous timing

or call the support function mkl_verbose(int mode):

  CPU application GPU application
Call mkl_verbose(0) to disable Verbose to disable Verbose
Call mkl_verbose(1) to enable Verbose to enable Verbose without timing
Call mkl_verbose(2) to enable Verbose to enable Verbose with synchronous timing

Verbose with CPU Applications

Verbose output will be consisted of version information line and call description lines for CPU.

For CPU applications, you can enable Intel® oneAPI Math Kernel Library (oneMKL) Verbose mode in these domains:

  • BLAS (and BLAS-like extensions)
  • LAPACK
  • ScaLAPACK (selected functionality)
  • FFT

Verbose with GPU Applications

The verbose feature is enabled for GPU applications that uses DPC++ API or C/Fortran API with OpenMP offload. When used with GPU applications, verbose allows the measurement of execution time to be enabled or disabled with verbose mode. Timing is taken synchronously, so if verbose is enabled with timing, kernel executions will become synchronous (previous kernel will block later kernels)

Verbose output will be consisted of version information line, GPU information lines, and call description lines for GPU.

NOTE:
Timing for GPU applications is reported for overall execution. For selected functionality device execution time can be also reported if the input queue was created with profiling information (see the oneAPI GPU Optimization Guide).

For GPU applications, you can enable Intel® oneAPI Math Kernel Library (oneMKL) Verbose mode in these domains:

  • BLAS (and BLAS-like extensions)
  • LAPACK
  • FFT

For Both CPU and GPU Verbose

Both enabling and disabling of the Verbose mode using the function call takes precedence over the environment setting. For a full description of the mkl_verbose function, see either the Intel® oneAPI Math Kernel Library Developer Reference for C or the Intel® oneAPI Math Kernel Library Developer Reference for Fortran. Both references are available in the Intel® Software Documentation Library.

Intel® oneAPI Math Kernel Library (oneMKL) Verbose mode is not a thread-local but a global state. In other words, if an application changes the mode from multiple threads, the result is undefined.

warning:

The performance of an application may degrade with the Verbose mode enabled, especially when the number of calls to verbose-enabled functions is large, because every call to a verbose-enabled function requires an output operation.

Parent topic: Managing Output
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