The Intel® oneAPI Math Kernel Library (oneMKL) helps you achieve maximum performance with a math computing library of highly optimized, extensively parallelized routines for CPU and GPU. The library has C and Fortran interfaces for most routines on CPU, and DPC++ 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 DPC++ on CPU and GPU (Refer to the Intel® oneAPI Math Kernel Library—Data Parallel C++ Developer Reference for more details.)
Visit the Release Notes page for the Known Issues and most up-to-date information.
Visit the Intel® oneAPI Math Kernel Library System Requirements page for system requirements.
Visit the Get Started with the Intel® oneAPI DPC++ Compiler for DPC++ Compiler requirements.
Download Intel® oneAPI Math Kernel Library from the oneAPI base toolkit.
Select a function or routine from oneMKL that is best suited for your problem. Use these resources:
Resource Link | Contents |
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Intel® oneMKL Developer Guide for Linux* |
The Developer Guide contains detailed information on several topics including:
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Intel® oneMKL Developer Reference - C Language |
The Developer Reference (in C, Fortran, and DPC++ formats) contains detailed descriptions of the functions and interfaces for all library domains. |
Intel® Math Kernel Library Function Finding Advisor | Use the LAPACK Function Finding Advisor to explore LAPACK routines that are useful for a particular problem. For example, if you specify an operation as:
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Use the Intel® MKL Link Line Advisor to configure the link command according to your program features.
Some limitations and additional requirements:
Intel® oneAPI Math Kernel Library for DPC++ only supports using the mkl_intel_ilp64 interface library and sequential or TBB threading.
For DPC++ interfaces with static linking on Linux |
dpcpp -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 ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -lsycl -lOpenCL -lpthread -ldl -lm For example, building/statically linking main.cpp with ilp64 interfaces and TBB threading: dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I${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 ${MKLROOT}/lib/intel64/libmkl_core.a -Wl,--end-group -lsycl -lOpenCL -ltbb -lpthread -ldl -lm |
For DPC++ interfaces with dynamic linking on Linux |
dpcpp -DMKL_ILP64 <typical user includes and linking flags and other libs> -L${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 TBB threading: dpcpp -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 -lm |
For DPC++ interfaces with static linking on Windows |
dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 <typical user includes and linking flags and other libs> mkl_sycl.lib -static -lmkl_intel_ilp64.lib -lmkl_<sequential|tbb_thread>.lib -lmkl_core.lib -lsycl -lOpenCL For example, building/statically linking main.cpp with ilp64 interfaces and TBB threading: dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I”%MKLROOT%\include” main.cpp -lmkl_sycl.lib -lmkl_intel_ilp64.lib -lmkl_tbb_thread.lib -lmkl_core.lib -lsycl -lOpenCL |
For DPC++ interfaces with dynamic linking on Windows |
dpcpp -DMKL_ILP64 <typical user includes and linking flags and other libs> mkl_sycl_dll.lib -lmkl_intel_ilp64_dll.lib -lmkl_<sequential|tbb_thread>_dll.lib -lmkl_core_dll.lib -lsycl -lOpenCL For example, building/dynamically linking main.cpp with ilp64 interfaces and TBB threading: dpcpp -fsycl-device-code-split=per_kernel -DMKL_ILP64 -I”%MKLROOT%\include” main.cpp -L”%MKLROOT%\lib\intel64” -lmkl_sycl_dll.lib -lmkl_intel_ilp64_dll.lib -lmkl_tbb_thread_dll.lib -lmkl_core_dll.lib -lsycl -lOpenCL |
For C Interfaces with OpenMP Offload Support |
Use the classic Intel® MKL interface, threading and core libraries as recommended in the Intel® Math Kernel Library Link Line Advisor tool and add “-lOpenCL” to the link line. See the C OpenMP Offload Developer Guide for build/link options that may also be necessary like “-fiopenmp” and “-fopenmp-targets=spir64”. For example, building/ dynamically linking main.cpp on Linux with ilp64 interfaces and OpenMP threading:
Note, these two options (-fiopenmp, -fopenmop-targets=spir64) are required for build and link line on Windows as well. |
Resource |
Description |
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Intel MKL training courses | The online training site is an excellent resource for learning Intel® MKL basics with Get Started guides, videos, tutorials, webinars, and technical articles. |
Tutorial: Using Intel® Math Kernel Library for Matrix Multiplication: |
This tutorial demonstrates how you can use the Intel® MKL 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 Intel® MKL 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:
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Other Intel® MKL Documentation | Related documentation, such as performance data, application notes, and examples. |
Intel® oneAPI Math Kernel Library | The Intel® oneAPI Math Kernel Library (oneMKL) product page. See this page for support and online documentation. |
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