Proba: A High-Performance, Low-Traffic Probabilistic Spatial Memory Streaming Prefetcher
Spatial Memory Streaming (SMS) prefetchers exploit recurring spatial access patterns across memory regions to predict future accesses. Despite their effectiveness, existing SMS-based designs suffer from coarse control over aggressiveness and limited adaptability to dynamic program behavior and historical patterns. In this work, we present Proba, a feedback-directed SMS-based prefetcher that integrates probabilistic pattern updates, marginal accuracy decision-making, and pattern accumulation over several trigger features to achieve fine-grained accuracy-coverage trade-offs. When accuracy deteriorates, Proba moderates its aggressiveness to prioritize accurate prefetches; conversely, when accuracy rises, it expands its coverage. Proba achieves higher speedups than any other SMS-based prefetcher while being vastly more efficient in DRAM traffic. This culminates in a large performance gap between Proba and other techniques in multicore and DRAM-constrained scenarios without harming single-threaded throughput.