Spatio-Temporal Memory Streaming

Tuesday March 25, 2008
Hamerschlag Hall D-210
4:00 pm

Stephen Somogyi
Carnegie Mellon University

The memory system remains a key performance bottleneck in modern server systems. While recent prefetching/streaming proposals have demonstrated effectiveness at hiding long memory access latencies on certain commercial workloads, no single technique is effective for online transaction processing, decision support and web serving.

In this talk, I will present Spatio-Temporal Memory Streaming (STEMS). STEMS builds upon work in both spatial and temporal memory streaming while exploiting new observations about repetition within and across spatial layouts. STEMS dynamically reconstructs a total sequence of predicted memory accesses by interleaving small-scale spatial predictions into a longer, recorded sequence that spans distinct regions of memory. Over our suite of commercial workloads, STEMS achieves similar or higher prediction coverage than either spatial or temporal memory streaming alone, while requiring less predictor storage than temporal memory streaming.

Stephen Somogyi is a Ph.D. candidate in Electrical and Computer Engineering at Carnegie Mellon University, working with Prof. Babak Falsafi. His research interests focus on memory streaming techniques to improve the performance of future computer systems. He will graduate later this year.