Name
Abstract | ||
|---|---|---|
We investigate to which extent data partitioning can help improve the performance of software transactional memory (STM). Our main idea is that the access patterns of the various data structures of an application might be sufficiently different so that it would be beneficial to tune the behavior of the STM for individual data partitions. We evaluate our approach using standard transactional memory benchmarks. We show that these applications contain partitions with different characteristics and, despite the runtime overhead introduced by partition tracking and dynamic tuning, that partitioning provides significant performance improvements. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1378533.1378562 | SPAA |
Keywords | Field | DocType |
main idea,dynamic tuning,various data structure,individual data partition,software transactional memory,extent data partitioning,different characteristic,access pattern,standard transactional memory benchmarks,significant performance improvement,automatic data,data structure,transactional memory | Data structure,Software transactional memory,Computer science,Parallel computing,Transactional memory,Software,Data partitioning,Transactional leadership,Distributed computing | Conference |
Citations | PageRank | References |
20 | 0.75 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Torvald Riegel | 1 | 523 | 20.78 |
| Christof Fetzer | 2 | 2429 | 172.89 |
| Pascal Felber | 3 | 2432 | 178.76 |