NDLI: On Reducing False Sharing While Improving Locality on Shared Memory Multiprocessors

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Dynamic Linking on a Shared-Memory Multiprocessor

Reducing Cache Conflicts by Partitioning and Privatizing Shared Arrays

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On Reducing False Sharing While Improving Locality on Shared Memory Multiprocessors

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On Reducing False Sharing While Improving Locality on Shared Memory Multiprocessors

Content Provider	ACM Digital Library
Author	Choudhary, Alok Ramanujam, J. Banerjee, Prith Kandemir, Mahmut
Abstract	The performance of applications on large shared-memory multiprocessors with coherent caches depends on the interaction between the granularity of data sharing, the size of the coherence unit and the spatial locality exhibited by the applications, in addition to the amount of parallelism in the applications. Large coherence units are helpful in exploiting spatial locality, but worsen the effects of false sharing. We present a mathematical framework that allows a clean description of the relationship between spatial locality and false sharing. We first show how to identify a severe form of multiple-writer false sharing and then demonstrate the importance of the interaction between optimization techniques aimed at enhancing locality and the techniques oriented toward reducing false sharing. Given the conflicting requirements, a compiler based approach to this problem holds promise. We investigate the use of data transformations in addressing spatial locality and false sharing, and derives an approach that balances the impact of the two. Experimental results demonstrate that such a balanced approach outperforms those approaches that consider only one of these two issues. On an eight-processor SGI Origin 2000 system, our approach brings an additional 9% improvement over a powerful locality optimization technique that uses both loop and data transformations. Also, our approach obtains an additional 19% improvement over an optimization technique that is oriented specifically toward reducing false sharing. Our study also reveals that in addition to reducing synchronization costs and improving memory subsystem performance, obtaining large granularity parallelism also helps these two optimization techniques, namely, enhancing locality and reducing false sharing, be compatible.
ISBN	0769504256
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1999-10-12
Access Restriction	Subscribed
Subject Keyword	Data reuse cache locality false sharing loop and memory layout transformations shared-memory multiprocessors
Content Type	Text
Resource Type	Article

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On reducing false sharing while improving locality on shared memory multiprocessors (1999).

On reducing false sharing while improving locality on shared memory multiprocessors (1999)

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On reducing false sharing while improving locality on shared memory multiprocessors

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Reducing False Sharing on Shared Memory Multiprocessors

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Reducing false sharing on shared memory multiprocessors through compile time data transformations

On Reducing False Sharing While Improving Locality on Shared Memory Multiprocessors

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