Comparative evaluation of fine- and coarse-grain approaches for software distributed shared memory

Abstract

Symmetric multiprocessors (SMPs) connected with low-latency networks provide attractive building blocks for software distributed shared memory systems. Two distinct approaches have been used: the fine-grain approach that instruments application loads and stores to support a small coherence granularity, and the coarse-grain approach based on virtual memory hardware that provides coherence at a page granularity. Fine-grain systems offer a simple migration path for applications developed on hardware multiprocessors by supporting coherence protocols similar to those implemented in hardware. On the other hand, coarse-grain systems can potentially provide higher performance through more optimized protocols and larger transfer granularities, while avoiding instrumentation overheads. Numerous studies have examined each approach individually, but major differences in experimental platforms and applications make comparison of the approaches difficult. This paper presents a detailed comparison of two mature systems, Shasta and Cashmere, representing the fine- and coarse-grain approaches, respectively. Both systems are tuned to run on the same commercially available, state-of-the-art cluster of AlphaServer SMPs connected via a Memory Channel network. As expected, our results show that Shasta provides robust performance for applications tuned for hardware multiprocessors, and can better tolerate fine-grain synchronization. In contrast, Cashmere is highly sensitive to fine-grain synchronization, but provides a performance edge for applications with coarse-grain behavior. Interestingly, we found that the performance gap between the systems can often be bridged by program modifications that address coherence and synchronization granularity. In addition, our study reveals some unexpected results related to the interaction of current compiler technology with application instrumentation, and the ability of SMP-aware protocols to avoid certain performance disadvantages of coarse-grain approaches.