Vectorizing for a SIMdD DSP architecture

摘要

The Single Instruction Multiple Data (SIMD) model for finegrained parallelism was recently extended to support SIMD operations on disjoint vector elements. In this paper we demonstrate how SIMdD (SIMD on disjoint data) supports e#ective vectorization of digital signal processing (DSP) benchmarks, by facilitating data reorganization and reuse. In particular we show that this model can be adopted by a compiler to achieve nearoptimal performance for important classes of kernels.