The upgraded and enlarged Intel oneAPI Math Kernel Library supports numerical processing not only on CPUs but also on GPUs, FPGAs, and other accelerators that are now standard components of heterogeneous computing environments.
In order to assist you decide if upgrading from traditional Intel MKL is the better option for you, this blog will provide you with a brief summary of the maths library.
Why just oneMKL?
The vast array of mathematical functions in oneMKL can be used for a wide range of tasks, from straightforward ones like linear algebra and equation solving to more intricate ones like data fitting and summary statistics.
Several scientific computing functions, including vector math, fast Fourier transforms (FFT), random number generation (RNG), dense and sparse Basic Linear Algebra Subprograms (BLAS), Linear Algebra Package (LAPLACK), and vector math, can all be applied using it as a common medium while adhering to uniform API conventions. Together with GPU offload and SYCL support, all of these are offered in C and Fortran interfaces.
Additionally, when used with Intel Distribution for Python, oneAPI Math Kernel Library speeds up Python computations (NumPy and SciPy).
Intel MKL Advanced with oneMKL
A refined variant of the standard Intel MKL is called oneMKL. What sets it apart from its predecessor is its improved support for SYCL and GPU offload. Allow me to quickly go over these two distinctions.
GPU Offload Support for oneMKL
GPU offloading for SYCL and OpenMP computations is supported by oneMKL. With its main functionalities configured natively for Intel GPU offload, it may thus take use of parallel-execution kernels of GPU architectures.
oneMKL adheres to the General Purpose GPU (GPGPU) offload concept that is included in the Intel Graphics Compute Runtime for OpenCL Driver and oneAPI Level Zero. The fundamental execution mechanism is as follows: the host CPU is coupled to one or more compute devices, each of which has several GPU Compute Engines (CE).
SYCL API for oneMKL
OneMKL’s SYCL API component is a part of oneAPI, an open, standards-based, multi-architecture, unified framework that spans industries. (Khronos Group’s SYCL integrates the SYCL specification with language extensions created through an open community approach.) Therefore, its advantages can be reaped on a variety of computing devices, including FPGAs, CPUs, GPUs, and other accelerators. The SYCL API’s functionality has been divided into a number of domains, each with a corresponding code sample available at the oneAPI GitHub repository and its own namespace.
OneMKL Assistance for the Most Recent Hardware
On cutting-edge architectures and upcoming hardware generations, you can benefit from oneMKL functionality and optimizations. Some examples of how oneMKL enables you to fully utilize the capabilities of your hardware setup are as follows:
It supports the 4th generation Intel Xeon Scalable Processors’ float16 data type via Intel Advanced Vector Extensions 512 (Intel AVX-512) and optimised bfloat16 and int8 data types via Intel Advanced Matrix Extensions (Intel AMX).
It offers matrix multiply optimisations on the upcoming generation of CPUs and GPUs, including Single Precision General Matrix Multiplication (SGEMM), Double Precision General Matrix Multiplication (DGEMM), RNG functions, and much more.
For a number of features and optimisations on the Intel Data Centre GPU Max Series, it supports Intel Xe Matrix Extensions (Intel XMX).
For memory-bound dense and sparse linear algebra, vector math, FFT, spline computations, and various other scientific computations, it makes use of the hardware capabilities of Intel Xeon processors and Intel Data Centre GPUs.
Additional Terms and Context
The brief explanation of terminology provided below could also help you understand oneMKL and how it fits into the heterogeneous-compute ecosystem.
The C++ with SYCL interfaces for performance math library functions are defined in the oneAPI Specification for oneMKL. The oneMKL specification has the potential to change more quickly and often than its implementations.
The specification is implemented in an open-source manner by the oneAPI Math Kernel Library (oneMKL) Interfaces project. With this project, we hope to show that the SYCL interfaces described in the oneMKL specification may be implemented for any target hardware and math library.
The intention is to gradually expand the implementation, even though the one offered here might not be the complete implementation of the specification. We welcome community participation in this project, as well as assistance in expanding support to more math libraries and a variety of hardware targets.
With C++ and SYCL interfaces, as well as comparable capabilities with C and Fortran interfaces, oneMKL is the Intel product implementation of the specification. For Intel CPU and Intel GPU hardware, it is extremely optimized.
Next up, what?
Launch oneMKL now to begin speeding up your numerical calculations like never before! Leverage oneMKL’s powerful features to expedite math processing operations and improve application performance while reducing development time for both current and future Intel platforms.
Keep in mind that oneMKL is rapidly evolving even while you utilize the present features and optimizations! In an effort to keep up with the latest Intel technology, we continuously implement new optimizations and support for sophisticated math functions.
They also invite you to explore the AI, HPC, and Rendering capabilities available in Intel’s software portfolio that is driven by oneAPI.
