Scaling irregular array-type reductions in OmpSs

Abstract

Array-type reductions represent a frequently occurring algorithmic pattern in many scientific applications. A special case occurs if array elements are accessed in a non-linear, often random manner, which makes their concurrent and scalable execution difficult. In this work we present a new approach that consists of language- and runtime support to facilitate programming and delivers high scalability on modern shared-memory systems for such irregular array-type reductions. A reference implementation in OmpSs, a task-parallel programming model, shows promising results with speed-ups up to 15x on the Intel Xeon processor.