Hardware Prediction for Data Coherency of Scientific Codes on DSM

This paper proposes a hardware mechanism for reducing coherency overhead occurring in scientific computations within DSM systems. A first phase aims at detecting, in the address space regular patterns (called streams) of coherency events (such as requests for exclusive, shared or invalidation). Once a stream is detected at a loop level, regularity of data access can be exploited at the loop level (spatial locality) but also between loops (temporal locality). We present a hardware mechanism capable of detecting and exploiting efficiently these regular patterns. Expectable benefits as well as hardware complexity are discussed and the limited drawbacks and potential over-heads are exposed. For a benchmarks suite of typical scientific applications results are very promising both in terms of coherency streams and the effectiveness of our optimizations.