Mixed data and task parallelism with HPF and PVM

Abstract

We present a framework to design efficient and portable HPF applications which exploit a mixture of task and data parallelism. According to the framework proposed, data parallelism is restricted within HPF modules, and task parallelism is achieved by the concurrent execution of several data-parallel modules cooperating through COLTHPF, a coordination layer implemented on top of PVM. COLTHPF can be used independently of the HPF compilation system exploited, and it allows instances of cooperating HPF tasks to be created either statically or at run-time. We claim that COLTHPF can be exploited by means of a simple skeleton-based coordination language and associated compiler to easily express mixed data and task parallel applications runnable on either multicomputers or cluster of workstations. We used a physics application as a test case of our approach for mixing task and data parallelism, and we present the results of several experiments conducted on a cluster of Linux SMPs.