Exploiting Data Locality in Adaptive Architectures

The speed of processors increases much faster than the memory access time. This makes memory accesses expensive. To meet this problem, cache hierarchies are introduced to serve the processor with data. However, the effectiveness of caches depends on the amount of locality in the application’s memory access pattern. The behavior of various programs differs greatly in terms of cache miss characteristics, access patterns and communication intensity. Therefore a computer built for many different computational tasks potentially benefits from dynamically adapting to the varying needs of the applications. This thesis shows that a cc-NUMA multiprocessor with data migration and replication optimizations efficiently exploits the temporal locality of algorithms. The performance of the self-optimizing system is similar to a system with a perfect initial thread and data placement. Data locality optimizations are not for free. Large cache line coherence protocols improve spatial locality but yield increases in false sharing misses for many applications. Prefetching techniques that reduce the cache misses often lead to increased address and data traffic. Several techniques introduced in this thesis efficiently avoid these drawbacks. The bundling technique reduces the coherence traffic in multiprocessor prefetchers. This is especially important in snoop-based systems where the coherence bandwidth is a scarce resource. Bundled prefetchers manage to reduce both the cache miss rate and the coherence traffic compared with non-prefetching protocols. The most efficient bundled prefetching protocol studied, lowers the cache misses by 27 percent and the address snoops by 24 percent relative to a non-prefetching protocol on average for all examined applications. Another proposed technique, capacity prefetching, avoids false sharing misses by distinguishing between cache lines involved in communication from non-communicating cache lines at run-time.