Shared virtual memory clusters: Bridging the cost-performance gap between SMPs and hardware DSM systems

Although the shared memory abstraction is gaining ground as a programming abstraction for parallel computing, the main platforms that support it, small-scale symmetric multiprocessors (SMPs) and hardware cache-coherent distributed shared memory systems (DSMs), seem to lie inherently at the extremes of the cost-performance spectrum for parallel systems. In this paper we examine if shared virtual memory (SVM) clusters can bridge this gap by examining how application performance scales on a state-of-the-art shared virtual memory cluster. We find that: (i) The level of application restructuring needed is quite high compared to applications that perform well on a DSM system of the same scale and larger problem sizes are needed for good performance. (ii) However, surprisingly, SVM performs quite well for a fairly wide range of applications, achieving at least half the parallel efficiency of a high-end DSM system at the same scale and often much more.