Exploiting Mixed Simd Parallelism By Reducing Data Reorganization Overhead

Existing loop vectorization techniques can exploit either intra- or inter-iteration SIMD parallelism alone in a code region if one part of the region vectorized for one type of parallelism has data dependences (called mixed-parallelism-inhibiting dependences) on the other part of the region vectorized for the other type of parallelism. In this paper, we consider a class of loops that exhibit both types of parallelism (i.e., mixed SIMD parallelism) in its code regions that contain mixed-parallelism-inhibiting data dependences. We present a new compiler approach for exploiting such mixed SIMD parallelism effectively by reducing the data reorganization overhead incurred when one type of parallelism is switched to the other. Our auto-vectorizer is simple and has been implemented in LLVM (3.5.0). We evaluate it on seven benchmarks with mixed SIMD parallelism selected from SPEC and NAS benchmark suites and demonstrate its performance advantages over the state-of-the-art.