Name
Abstract | ||
|---|---|---|
Data movement has a significant impact on program performance. For multithread programs, this impact is amplified, since different threads often interfere with each other by competing for shared cache space. However, recent de facto locality metrics consider either sequential execution only, or derive locality for multithread programs in an inefficient way, i.e. exhaustive simulation.
This paper presents PLUM, a compiler solution for time-scale locality analysis for parallel programs. Experiments demonstrate that the prediction accuracy is 93.97% on average. PLUM is the first tool that analyzes data locality for parallel programs during compile time; in addition, it provides an approach for efficiently studying the representative interleaving pattern for parallel executions.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3332466.3374510 | PPoPP '20: 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
San Diego
California
February, 2020 |
Keywords | DocType | ISBN |
Static analysis, Locality, Multithread | Conference | 978-1-4503-6818-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fangzhou Liu | 1 | 125 | 10.67 |
| Dong Chen | 2 | 3 | 2.44 |
| Wesley Smith | 3 | 0 | 1.01 |
| Chen Ding | 4 | 749 | 43.96 |