Introduction
Modern CPU and GPU cores use single instruction, multiple data (SIMD) execution units to achieve higher performance and power efficiency. The underlying SIMD hardware is exposed via instructions such as SSE, AVX, AVX2, AVX-512, and those in the Intel® Xe Architecture Gen12 ISA. While using these directly is an option, their low-level nature severely limits portability and proves unattractive for most projects.
To provide a more portable and easier to use interface for programmers, three avenues are explored in this article: auto-vectorization, programmer-guided SIMD vectorization through language constructs or programmer hints, and a SIMD data-parallel library approach. We provide an overview of these methods and show SIMD vectorization evolution in the LLVM and GCC compilers through code examples. We also examine a couple of vectorization techniques in the LLVM and GCC compilers to achieve optimal performance on Intel® Xeon® processors and Intel Xe Architecture GPUs.