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Automatic Data Partitioning in Software Transactional Memories

Torvald Riegel, Christof Fetzer & Pascal Felber

Résumé 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.
   
Mots-clés Algorithms, Performance
   
Citation T. Riegel, et al., "Automatic Data Partitioning in Software Transactional Memories," in 20th ACM Symposium on Parallelism in Algorithms and Architectures, Munich, GERMANY, 2008, p. 152-159.
   
Type Actes de congrès (Anglais)
Nom de la conférence 20th ACM Symposium on Parallelism in Algorithms and Architectures (Munich, GERMANY)
Date de la conférence 2008
Editeur commercial Assoc Computing Machinery
Pages 152-159