Name
Title | ||
|---|---|---|
Understanding Why Correlation Profiling Improves the Predictability of Data Cache Misses in Nonnumeric Applications |
Abstract | ||
|---|---|---|
Latency-tolerance techniques offer the potential for bridging the ever-increasing speed gap between the memory subsystem and today's high-performance processors. However, to fully exploit the benefit of these techniques, one must be careful to apply them only to the dynamic references that are likely to suffer cache misses驴otherwise the runtime overheads can potentially offset any gains. In this paper, we focus on isolating dynamic miss instances in nonnumeric applications, which is a difficult but important problem. Although compilers cannot statically analyze data locality in nonnumeric applications, one viable approach is to use profiling information to measure the actual miss behavior. Unfortunately, the state-of-the-art in cache miss profiling (which we call summary profiling) is inadequate for references with intermediate miss ratios驴it either misses opportunities to hide latency, or else inserts overhead that is unnecessary. To overcome this problem, we propose and evaluate a new profiling technique that helps predict which dynamic instances of a static memory reference will hit or miss in the cache: correlation profiling. Our experimental results demonstrate that roughly half of the 21 nonnumeric applications we study can potentially enjoy significant reductions in memory stall time by exploiting at least one of the three forms of correlation profiling we consider: control-flow correlation, self correlation, and global correlation. In addition, our detailed case studies illustrate that self correlation succeeds because a given reference's cache outcomes often contain repeated patterns and control-flow correlation succeeds because cache outcomes are often call-chain dependent. Finally, we suggest a number of ways to exploit correlation profiling in practice and demonstrate that software prefetching can achieve better performance on a modern superscalar processor when directed by correlation profiling rather than summary profiling information. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/12.844349 | IEEE Trans. Computers |
Keywords | Field | DocType |
self correlation,data cache miss,control-flow correlation,cache outcome,new profiling technique,profiling information,global correlation,correlation profiling,dynamic instance,summary profiling,nonnumeric applications,nonnumeric application,data analysis,software performance,information analysis,control flow | Locality,Cache,Profiling (computer programming),Computer science,Latency (engineering),Parallel computing,Exploit,Real-time computing,Static random-access memory,Compiler,Offset (computer science) | Journal |
Volume | Issue | ISSN |
49 | 4 | 0018-9340 |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Todd C. Mowry | 1 | 3021 | 253.75 |
| Chi-Keung Luk | 2 | 2537 | 116.49 |