Automatic data partitioning in software transactional memories

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Automatic data partitioning in software transactional memories

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ACM Symposium on Parallel Algorithms and Architectures archive
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures table of contents

Munich, Germany

SESSION: Special track -- transactional memory table of contents

Pages 152-159

Year of Publication: 2008

ISBN:978-1-59593-973-9

Authors

Torvald Riegel	Dresden University of Technology, Dresden, Germany
Christof Fetzer	Dresden University of Technology, Dresden, Germany
Pascal Felber	University of Neuchatel, Neuchatel, Switzerland

Sponsors

ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGARCH: ACM Special Interest Group on Computer Architecture

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ACM New York, NY, USA

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Downloads (6 Weeks): 10, Downloads (12 Months): 118, Citation Count: 1

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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