Methods for Creating and Exploiting Data Locality

Abstract: The gap between processor speed and memory latency has led to the use of caches in the memory systems of modern computers. Programs must use the caches efficiently and exploit data locality for maximum performance. Multiprocessors, built from many processing units, are becoming commonplace not only in large servers but also in smaller systems such as personal computers. Multiprocessors require careful data locality optimizations since accesses from other processors can lead to invalidations and false sharing cache misses. This thesis explores hardware and software approaches for creating and exploiting temporal and spatial locality in multiprocessors.We propose the capacity prefetching technique, which efficiently reduces the number of cache misses but avoids false sharing by distinguishing between cache lines involved in communication from non-communicating cache lines at run-time. Prefetching techniques often lead to increased coherence and data traffic. The new bundling technique avoids one of these drawbacks and reduces the coherence traffic in multiprocessor prefetchers. This is especially important in snoop-based systems where the coherence bandwidth is a scarce resource.Most of the studies have been performed on advanced scientific algorithms. This thesis demonstrates that a cc-NUMA multiprocessor, with hardware data migration and replication optimizations, efficiently exploits the temporal locality in such codes. We further present a method of parallelizing a multigrid Gauss-Seidel partial differential equation solver, which creates temporal locality at the expense of increased communication. Our conclusion is that on modern chip multiprocessors, it is more important to optimize algorithms for data locality than to avoid communication, since communication can take place using a shared cache.