Keynote - SIMD Vectorization Essentials: Learnings, Successes and Advances
SIMD Vectorization has received significant attention in the past decade as one of the most important methods to accelerate scientific applications, media and embedded applications on SIMD architectures such as Intel SSE, AVX, IBM AltiVec and ARM Neon. However, the recent proliferation of modern SIMD architectures poses new constraints such as control flow divergence , memory access divergence, data alignment, mixed data type, and wider fixed-length nature of SIMD vectors, that demand advanced SIMD vectorization compiler technologies and SIMD vectorization friendly language extensions. In this talk, we take a look back on what we have learned in the past decades, and what we have achieved on the path of successful SIMD vectorization for exploiting effective SIMD parallelism in real large applications in the past few years at Intel. We share Intel’s vision on explicit SIMD programming model and compiler technology evolution for SIMD vectorization.