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| Content Provider | ACM Digital Library |
|---|---|
| Author | Tarvo, Alexander Reiss, Steven P. |
| Abstract | Predicting the performance of a computer program facilitates its efficient design, deployment, and problem detection. However, predicting performance of multithreaded programs is complicated by complex locking behavior and concurrent usage of computational resources. Existing performance models either require running the program in many different configurations or impose restrictions on the types of programs that can be modeled. This paper presents our approach towards building performance models that do not require vast amounts of training data. Our models are built using a combination of queuing networks and probabilistic call graphs. All necessary information is collected using static and dynamic analyses of a single run of the program. In our experiments these models were able to accurately predict performance of different types of multithreaded programs and detected those configurations that result in the programs' high performance. |
| Starting Page | 217 |
| Ending Page | 228 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450312028 |
| DOI | 10.1145/2188286.2188320 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-04-22 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Configuration Simulation Performance Modeling |
| Content Type | Text |
| Resource Type | Article |
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