Dynamic multigrain parallelization on the cell broadband engine

Abstract

This paper addresses the problem of orchestrating and scheduling parallelism at multiple levels of granularity on heterogeneous multicore processors. We present mechanisms and policies for adaptive exploitation and scheduling of layered parallelism on the Cell Broadband Engine. Our policies combine event-driven task scheduling with malleable loop-level parallelism, which is exploited from the runtime system whenever task-level parallelism leaves idle cores. We present a scheduler for applications with layered parallelism on Cell and investigate its performance with RAxML, an application which infers large phylogenetic trees, using the Maximum Likelihood (ML) method. Our experiments show that the Cell benefits significantly from dynamic methods that selectively exploit the layers of parallelism in the system, in response to workload fluctuation. Our scheduler out performs the MPI version of RAxML, scheduled by the Linux kernel, by up to a factor of 2.6. We are able to execute RAxMLon one Cell four times faster than on a dual-processor system with Hyperthreaded Xeon processors, and 5--10% faster than on a single-processor system with a dual-core, quad-thread IBM Power5processor.