Reusing Data Reorganization for Efficient SIMD Parallelization of Adaptive Irregular Applications

Abstract

Applying SIMD parallelization to irregular applications with non-continuous and data-dependent memory accesses is challenging. While an application involving a static pattern of indirect accesses (across iterations) can be accelerated by data transformations, such techniques are no longer feasible if the indirect access patterns change over time. In this paper, we propose an indexing method that facilitates the reuse of data reorganization for efficient SIMD parallelization of dynamic irregular applications. This indexing approach is first applied on a class of vertex-centric graph algorithms where the set of active vertices varies over the execution -- the indexing method helps maintain the set of active edges. Next, we focus on unstructured particle interaction applications in which the edges change adaptively, and present an incremental indexing method. In our experimental evaluation, the speedups achieved by utilizing SIMD on graph applications range from 3.04× to 7.19×, and between 2.54× to 4.43× for molecular dynamics.