Get Started Guide
The Intel® oneAPI Math Kernel Library (oneMKL) helps you achieve maximum performance with a math computing library of highly optimized and extensively parallelized routines for CPU and GPU. The library has C and Fortran interfaces for most routines on CPU and SYCL interfaces for some routines on both CPU and GPU. You can find comprehensive support for several math operations in various interfaces including:
For C and Fortran on CPU
For SYCL on CPU and GPU (Refer to the Intel® oneAPI Math Kernel Library—Data Parallel C++ Developer Reference for more details.)
Linear algebra
Fast Fourier Transforms (FFT)
Random number generators
Selected Vector Math functionality
To learn about the Known Issues and get other up-to-date information, visit the Release Notes page.
For system requirements, go to the Intel® oneAPI Math Kernel Library System Requirements page.
For DPC++ Compiler requirements, visit the Get Started with the Intel® oneAPI DPC++/C++ Compiler.
Download Intel® oneAPI Math Kernel Library from the Intel® oneAPI Base Toolkit.
For Python distributions, refer to Intel® Distribution for Python*.
For Python distributions, note the following limitation:
The oneMKL devel package (mkl-devel) for PIP distribution on Linux* does not provide dynamic libraries symlinks (for more information, see PIP GitHub issue #5919).
In the case of dynamic or single dynamic library linking with oneMKL devel package (for more information see oneMKL Link Line Advisor), you must modify link line with the full names and versions of the oneMKL libraries.
For information about compiling and linking with the pkg-config tool, refer to the Intel® oneAPI Math Kernel Library and pkg-config tool.
Here is a oneMKL link line example with the oneAPI Base Toolkit via symlinks:
Linux:
icx app.obj -L${MKLROOT}/lib/intel64 -lmkl_intel_lp64-lmkl_intel_thread -lmkl_core -liomp5 -lpthread -lm -ldl
The oneMKL link line example with PIP devel package via libraries full names and versions:
Linux:
icx app.obj ${MKLROOT}/lib/intel64/libmkl_intel_lp64.so.2 ${MKLROOT}/lib/intel64/libmkl_intel_thread.so.2 ${MKLROOT}/lib/intel64/libmkl_core.so.2 -liomp5 -lpthread -lm
-ldl
Select a function or routine from oneMKL that is best suited for your problem. Use these resources:
| Resource Link | Contents |
|---|---|
The Developer Guide contains detailed information on several topics including:
|
|
oneMKL Developer Reference - C |
The Developer Reference (in C, Fortran, and DPC++ formats) contains detailed descriptions of the functions and interfaces for all library domains. |
Use the oneMKL Link Line Advisor to configure the link command according to your program features.
Before linking your code, consider the following limitations and additional requirements:
For SYCL interfaces with static linking on Linux |
icpx -fsycl -fsycl-device-code-split=per_kernel -DMKL_ILP64 <typical user includes and linking flags and other libs> ${MKLROOT}/lib/intel64/libmkl_sycl.a -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_<sequential|tbb_thread>.a ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -lsycl -lOpenCL -lpthread -ldl -lm For example, building/statically linking main.cpp with ilp64 interfaces and oneTBB threading: icpx -fsycl -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I${MKLROOT}/include main.cpp ${MKLROOT}/lib/intel64/libmkl_sycl.a -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_tbb_thread.a ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -L${TBBROOT}/lib/intel64/gcc4.8 -ltbb -lsycl -lOpenCL -lpthread -lm -ldl |
For SYCL interfaces with dynamic linking on Linux |
icpx -fsycl -DMKL_ILP64 <typical user includes and linking flags and other libs> -L${MKLROOT}/lib/intel64 -lmkl_sycl -lmkl_intel_ilp64 -lmkl_<sequential|tbb_thread> -lmkl_core -lsycl -lOpenCL -lpthread -ldl -lm For example, building/dynamically linking main.cpp with ilp64 interfaces and oneTBB threading including all SYCL domains: icpx -fsycl -DMKL_ILP64 -I${MKLROOT}/include main.cpp -L${MKLROOT}/lib/intel64 -lmkl_sycl -lmkl_intel_ilp64 -lmkl_tbb_thread -lmkl_core -lsycl -lOpenCL -ltbb -lpthread -ldl -lmOr the same configuration with the BLAS SYCL domain only (note that libraries specific to the SYCL domain are aligned with oneMKL domain namespaces): icpx -fsycl -DMKL_ILP64 -I${MKLROOT}/include main.cpp -L${MKLROOT}/lib/intel64 -lmkl_sycl_blas -lmkl_intel_ilp64 -lmkl_tbb_thread -lmkl_core -lsycl -lOpenCL -ltbb -lpthread -ldl -lm |
For SYCL interfaces with static linking on Windows |
icpx -fsycl -fsycl-device-code-split=per_kernel -DMKL_ILP64 <typical user includes and linking flags and other libs> "%MKLROOT%"\lib\intel64\mkl_sycl.lib mkl_intel_ilp64.lib mkl_<sequential|tbb_thread>.lib mkl_core_lib sycl.lib OpenCL.lib For example, building/statically linking main.cpp with ilp64 interfaces and oneTBB threading: icpx -fsycl -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I"%MKLROOT%\include" main.cpp"%MKLROOT%"\lib\intel64\mkl_sycl.lib mkl_intel_ilp64.lib mkl_tbb_thread.lib mkl_core.lib sycl.lib OpenCL.lib tbb.lib |
For SYCL interfaces with dynamic linking on Windows |
icx -fsycl -DMKL_ILP64 <typical user includes and linking flags and other libs> "%MKLROOT%"\lib\intel64\mkl_sycl_dll.lib mkl_intel_ilp64_dll.lib mkl_<sequential|tbb_thread>_dll.lib mkl_core_dll.lib tbb.lib sycl.lib OpenCL.lib Here is an example of building or dynamically linking main.cpp with ilp64 interfaces and oneTBB threading including all SYCL domains: icx -fsycl -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I"%MKLROOT%\include" main.cpp "%MKLROOT%"\lib\intel64\mkl_sycl_dll.lib mkl_intel_ilp64_dll.lib mkl_tbb_thread_dll.lib mkl_core_dll.lib tbb.lib sycl.lib OpenCL.lib Or the same configuration with the BLAS SYCL domain only (note that libraries specific to the SYCL domain are aligned with oneMKL domain namespaces): icx -fsycl -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I"%MKLROOT%\include" main.cpp "%MKLROOT%"\lib\intel64\mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_tbb_thread_dll.lib mkl_core_dll.lib tbb.lib sycl.lib OpenCL.lib |
For C/Fortran Interfaces with OpenMP Offload Support |
Use the C/Fotran Intel® oneAPI Math Kernel Library interfaces with the OpenMP offload feature for GPU. Add the following changes to the C/Fortran oneMKL compile/link lines to enable the OpenMP offload feature for GPU:
For example, building/ dynamically linking main.cpp on Linux with ilp64 interfaces and OpenMP threading: For all other supported configurations, see Intel® oneAPI Math Kernel Library Link Line Advisor. |
Resource |
Description |
|---|---|
| oneMKL SYCL Samples in GitHub | These samples were designed to help you develop, offload, and optimize multi-architecture applications targeting CPUs and GPUs. |
Tutorial: Using Intel® oneAPI Math Kernel Library for Matrix Multiplication: |
This tutorial demonstrates how you can use oneMKL to multiply matrices, measure the performance of matrix multiplication, and control threading. |
| Intel® oneAPI Math Kernel Library (oneMKL) Release Notes | The release notes contain information specific to the latest release of oneMKL including new and changed features. The release notes include links to principal online information resources related to the release. You can also find information on:
|
| Intel® oneAPI Math Kernel Library | For support and online documentation, refer to this Intel® oneAPI Math Kernel Library (oneMKL) product page. |
| Intel® oneAPI Math Kernel Library Cookbook | The Intel® oneAPI Math Kernel Library contains many routines to help you solve various numerical problems, such as multiplying matrices, solving a system of equations, and performing a Fourier transform. |
| Notes for Intel® oneAPI Math Kernel Library Vector Statistics | This document includes an overview, a usage model and testing results of random number generators included in VS. |
| Intel® oneAPI Math Kernel Library Vector Statistics Random Number Generator Performance Data | Refer to this document for performance data obtained using VS (vector statistics), RNG (random number generators) including CPE (clocks per element), BRNG (basic random number generators), generated distribution generators, and length of generated vectors. |
| Intel® oneAPI Math Kernel Library Vector Mathematics Performance and Accuracy Data | Vector Mathematics (VM) computes elementary functions on vector arguments. VM includes a set of highly optimized implementations of computationally expensive core mathematical functions (power, trigonometric, exponential, hyperbolic, and others) that operate on vectors. |
| Application Notes for Intel® oneAPI Math Kernel Library Summary Statistics | Summary Statistics is a subcomponent of the Vector Statistics domain of Intel® oneAPI Math Kernel Library. Summary Statistics provides you with functions for initial statistical analysis, and offers solutions for parallel processing of multi-dimensional datasets. |
| LAPACK Examples | This document provides code examples for oneMKL LAPACK (Linear Algebra PACKage) routines. |
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks.
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Product and Performance Information |
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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex. Notice revision #20201201 |
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