Cooperative server clustering for a scalable GAS model on petascale cray XT5 systems

Abstract

Global Address Space (GAS) programming models are attractive because they retain the easy-to-use addressing model that is the characteristic of shared-memory style load and store operations. The scalability of GAS models depends directly on the design and implementation of runtime libraries on the targeted platforms. In this paper, we examine the memory requirement of a popular GAS run-time library, Aggregate Remote Memory Copy Interface (ARMCI) on petascale Cray XT 5 systems. Then we describe a new technique cooperative server clustering that enhances the memory scalability of ARMCI communication servers. In cooperative server clustering, ARMCI servers are organized into clusters, and cooperatively process incoming communication requests among them. A request intervention scheme is also designed to expedite the return of responses to the initiating processes. Our experimental results demonstrate that, with very little impact on ARMCI communication latency and bandwidth, cooperative server clustering is able to significantly reduce the memory requirement of ARMCI communication servers, thereby enabling highly scalable scientific applications. In particular, it dramatically reduces the total execution time of a scientific application, NWChem, by 45% on 2400 processes.