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Enhanced Speculative Parallelization via Incremental Recovery by Chen Tian; Changhui Lin; Min Feng; Rajiv Gupta is a Computer Science article available to read on EtoBox.

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The widespread availability of multicore systems has led to an increased interest in speculative parallelization of sequential programs using software-based thread level speculation. Many of the proposed techniques are implemented via state separation where non-speculative computation state is maintained separately from the speculative state of threads performing speculative computations. If speculation is successful, the results from speculative state are committed to non-speculative state. However, upon misspeculation, discard-all scheme is employed in which speculatively computed results of a thread are discarded and the computation is performed again. While this scheme is simple to implement, one disadvantage of discard-all is its inability to tolerate high misspeculation rates due to its high runtime overhead. Thus, it is not suitable for use in applications where misspeculation rates are input dependent and therefore may reach high levels. In this paper we develop an approach for incremental recovery in which, instead of discarding all of the results and reexecuting the speculative computation in its entirety, the computation is restarted from the earliest point at which a mi

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Author

Chen Tian; Changhui Lin; Min Feng; Rajiv Gupta

Publisher

ACM

Published

2011

Language

EN

Field

Computer Science (Physical Sciences)