Multi-level Adaptive Prefetching based on Performance Gradient Tracking
Luis M. Ramos, José Luis Briz, Pablo Ibáñez, Víctor Viñals · The journal of instruction-level parallelism · 2011
We introduce a multi-level prefetching framework with three setups, respectively aimed to minimize cost (Mincost), minimize losses in individual applications (Minloss) or maximize performance with moderate cost (Maxperf). Performance is boosted in all cases by a sequential tagged prefetcher in the L1 cache, with an effective static degree policy. In both cache levels (L1 and L2), we also apply prefetch filters. In the L2 cache we use a novel adaptive policy that selects the best prefetching degree within a fixed set of values, by tracking the performance gradient. Mincost resorts to sequential tagged prefetching in the L2 cache as well. Minloss relies on an accurate, home-made, correlating prefetcher (PDFCM, Differencial Finite Context Method Prefetcher). Maxperf maximizes performance at the expense of slight performance losses in a small number of benchmarks, by integrating a sequential tagged prefetcher with PDFCM in the L2 cache.