Name
Abstract | ||
|---|---|---|
Collecting hardware event counts is essential to understanding program execution behavior. Contemporary systems offer few Performance Monitoring Counters (PMCs), thus only a small fraction of hardware events can be monitored simultaneously. We present new techniques to acquire counts for all available hardware events with high accuracy by multiplexing PMCs across multiple executions of the same program, then carefully reconciling and merging the multiple profiles into a single, coherent profile. We present a new metric for assessing the similarity of statistical distributions of event counts and show that our execution profiling approach performs significantly better than Hardware Event Multiplexing.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3148054 | TACO |
Keywords | Field | DocType |
Hardware event monitoring, hardware event multiplexing, performance monitoring counters, task-parallel performance analysis | Performance monitoring,Profiling (computer programming),Computer science,Parallel computing,Real-time computing,Probability distribution,Computer hardware,Merge (version control),Fuse (electrical),Multiplexing | Journal |
Volume | Issue | ISSN |
14 | 4 | 1544-3566 |
Citations | PageRank | References |
1 | 0.38 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard Neill | 1 | 1 | 1.39 |
| Andi Drebes | 2 | 29 | 4.06 |
| Antoniu Pop | 3 | 198 | 14.36 |