Name
Abstract | ||
|---|---|---|
Scalable multiprocessors present special challenges to static software prefetching because on these systems the memory access latency is not completely determined at compile time. Furthermore, dynamic software prefetching cannot do much better because individual nodes on large-scale multiprocessors would tend to experience different remote memory delays over time. A fixed prefetch distance, even when computed at run-time, cannot perform well for the whole duration of a software pipeline. Here we present an adaptive scheme for software prefetching that makes it possible for nodes to dynamically change, not only the amount of prefetching, but the prefetch distance as well. We show how simple performance data collected by hardware monitors can allow programs to observe, evaluate and change their prefetching policies. Our results show that adaptive prefetching (APF) was capable of improving performance over static and dynamic prefetching by 10%–60%. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0167-8191(01)00085-0 | Parallel Computing |
Keywords | Field | DocType |
adaptive software,cache information,multiprocessor,software prefetching,adaptive execution,distributed shared memory,adaptive prefetching,scalable multiprocessors,software pipelining,data collection | Computer science,Cache,Latency (engineering),Compile time,Parallel computing,Multiprocessing,Software,Instruction prefetch,Distributed shared memory,Scalability | Journal |
Volume | Issue | ISSN |
27 | 9 | Parallel Computing |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daeyeon Park | 1 | 161 | 27.12 |
| Byeong Hag Seong | 2 | 9 | 1.66 |
| Rafael H. Saavedra | 3 | 151 | 20.56 |