![]() ![]() CES Edupack contains a unique set of teaching resources that support Materials Education across Engineering, Design, Science and Sustainable Development.ĬES Edupack for students External link. If you want to use CES Edupack, you can access their material yourself via the link below. If you give it a try, let me know how you get on in the comments section or via twitter.Since the summer of 2020, the University no longer provides CES Edupack for students through the student account. I was very excited by this recent update and hope that you are too. So Matrix-Matrix multiplication is faster on this hardware using AOCL but Cholesky decomposition is slower for this matrix size. For example, using the script laBench.m, and running on an Azure D16ads_v5 instance that exposes 8 cores of an AMD EPYC 7763 I found the following timings for 10,000 x 10,000 matrices: Intel MKL results (best of 3) It is not necessarily the case that one library always outperforms the other on any given piece of hardware. Any potential differences will depend on factors such as type of operation, matrix size and structure and exactly which CPU you are using. You should only expect to see performance differences in functions that make use of linear algebra. ![]() What performance differences can you expect to see? Of course we are also very interested in learning of any problems you encounter in trying this out. It may be the case that AOCL is faster than MKL for some operations and we are interested in hearing from you if this is the case. The reason you might do this, of course, is speed. Instructions for making the switch are given on this MATLAB Answers post. As such, R2022a continues to use MKL by default but users of both Intel and AMD hardware (On Windows and Linux) are able to switch to using the version of AOCL that has passed MathWorks qualification testing. Changing the default version of anything is not something MathWorks does lightly. AOCL is optionally available in MATLAB from R2022aĪs of R2022a, we have started shipping AOCL with MATLAB but it is not activated by default. Called the AMD Optimizing CPU Libraries, or AOCL for short, these are developed by AMD and targeted at their own hardware although, as with Intel MKL, they work on both AMD and Intel hardware. MKL works just fine on AMD processors as well but some of our users have been asking for official support for AMD's own accelerated implementations of these libraries. 'Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210611 for Intel(R) 64 architecture applications (CNR branch AVX512_E1)' 'Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210611 for Intel(R) 64 architecture applications (CNR branch AVX512_E1) supporting Linear Algebra PACKage (LAPACK 3.9.0)' ![]() Intel MKL has been MATLAB's provider of BLAS and LAPACK for a long time now. Another example is Intel’s Math Kernel Library (MKL) which, as the name suggests, is a library from Intel that provides highly optimized versions of BLAS and LAPACK for their hardware. I mentioned one example, OpenBLAS, in my post about the R2022a Apple Silicon beta version of MATLAB. The so-called reference BLAS and LAPACK define the user interfaces and give easy to read, unoptimized implementations of each of the operations.ĭifferent groups produce optimized implementations of these libraries using various strategies. One thing to know about BLAS and LAPACK libraries is that there are many implementations of them. Earlier this year we entered a new era of computing when the Frontier supercomputer demonstrated that it could operate at 1 Exaflop (10^18 flops). Something many of us at MathWorks find interesting about the results above is that the performance was then measured in Megaflops (10^6 floating point operations per second), thousands of times slower than the Gigaflops (10^9 flops) we expect from even the most modest laptop today. The red lines show the speed-up compared to MATLAB before LAPACK From Cleve's article in 2000 showing the speed of eigenvalue calculations back then. ![]()
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