Overcoming performance bottlenecks in using OpenMP on SMP clusters

Abstract

This paper presents a new parallel programming environment called ParADE to enable easy, portable, and high-performance computing for SMP clusters. Different from the prior studies, ParADE separates the programming model from the execution model: it enables shared-address-space programming while it realizes hybrid execution of message-passing and shared-address-space. To overcome the poor performance of conventional OpenMP on SDSM (Software Distributed Shared Memory), ParADE implements an intelligent OpenMP translator supporting efficient mutual exclusion and efficient page transmission. The experimental results on a Linux cluster demonstrate that ParADE reduces mutual exclusion overhead and overall execution time.