A Decomposition Approach for Optimizing Large-Scale Parallel Image Composition on Multi-Core MPP Systems

Abstract

In recent years, multi-core processor architecture has emerged as the predominant hardware architecture for high performance computing (HPC) systems. In addition, computational nodes based on SMP (symmetric multiprocessing) and NUMA (non-uniform memory architecture) have become increasingly common. Traditional parallel image composition algorithms were not primarily designed to take advantage of the combined message passing and shared address space parallelism provided by modern massively parallel processing (MPP) systems. This therefore might result in undesirable performance loss. In this study, we have investigated the use of a simple decomposition approach to take advantage of these different hardware characteristics for optimizing the parallel image composition process. Performance evaluation was carried out on a multi-core, multi-processor architecture based T2K Open Supercomputer, and we obtained encouraging results showing the effectiveness of the proposed approach. This approach also seems promising to tackle the large-scale image composition problem on nextgeneration HPC systems where an ever increasing number of processing cores are expected.