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BLAS was originally developed as a linear algebra library using Fortran, and was later ported to C/C++. As a core component of modern high-performance computing, it has formed a set of standards. There are open source implementations such as Netlib BLAS, GotoBLAS and its successor OpenBLAS. Commercially, each manufacturer has corresponding implementations for its own platform, such as Intel’s MKL, NVIDIA’s CUDA, AMD’s AOCL and ROCm. Some of them are optimized for CPU platforms, and some use GPU parallel acceleration. This article uses different BLAS libraries to implement matrix operations and analyzes the performance differences between different implementations.

Speaking of matrices, anyone who studies science and engineering will think of the fear of being dominated by linear algebra classes. Matrix multiplication operations are indispensable for various industrial and scientific research numerical calculations, and are also used in various benchmarking software. The time consumption of matrix multiplication operations is also an important indicator for judging the floating-point operation performance of computers. The purpose of this article is to verify the performance differences of various implementation methods through matrix multiplication operations, and compare the performance differences of different computing platforms to provide a reference for high-performance computing development.

Recently, I plan to refactor the code I wrote previously using Rust, which involves the question of how to choose a GUI interface. Rust has only been officially released for ten years, and is not as good as the old C/C++ in GUI development. There are many well-known and time-tested GUI interface libraries such as wxWidgets, qt, gtk+, etc. This article selects several Rust GUI libraries and simply implements a boundary layer calculator for horizontal comparison.