Abstract
Although prefetching concepts have been proposed for decades, new challenges are introduced by sophisticated system architecture and emerging applications. Large instruction windows coupled with out-of-order execution makes program data access sequence distorted from cache perspective. Big data applications stress memory subsystems heavily with their large working set sizes and complex data access patterns. To address such challenges, this work proposes a high performance hardware prefetching scheme, SelSMaP. SelSMaP is able to detect both regular and non-uniform stride patterns by taking the minimum observed address offset (called a reference stride) as a heuristic. We evaluated SelSMaP with CloudSuite workloads and SPEC CPU2006 benchmarks. SelSMaP achieves an average CloudSuite performance improvement of 30% over non-prefetching system. With one to two order of magnitude less storage and much less functional logic, SelSMaP outperforms the highest-performing prefetcher by 8.6% in CloudSuite workloads.