A meta-predictor framework for prefetching in object-based DSMs

Jean Christophe Beyler 1 Michael Klemm 2 Philippe Clauss 3 Michael Philippsen 2
3 CAMUS - Compilation pour les Architectures MUlti-coeurS
LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection, Inria Nancy - Grand Est
Abstract : Dynamic optimizers modify the binary code of programs at runtime by profiling and optimizing certain aspects of the execution. We present a completely software-based framework that dynamically optimizes programs for object-based distributed shared memory (DSM) systems on clusters. In DSM systems, reducing the number of messages between cluster nodes is crucial. Prefetching transfers data in advance from the storage node to the local node so that communication is minimized. Our framework uses a profiler and a dynamic binary rewriter that monitor the access behavior of the application and place prefetches where they are beneficial to speed up the application. In addition, we use two distinct predictors to handle different types of access patterns. A meta-predictor analyzes the memory access behavior and dynamically enables one of the predictors. Our system also adapts the number of prefetches per request to best fit the application's behavior. The evaluation shows that the performance of our system is better than the manual prefetching. The number of messages sent decreases by up to 90%. Performance gains of up to 80% can be observed on benchmarks.
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https://hal.inria.fr/inria-00504618
Contributor : Philippe Clauss <>
Submitted on : Tuesday, July 20, 2010 - 5:11:30 PM
Last modification on : Thursday, January 11, 2018 - 6:23:13 AM

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Jean Christophe Beyler, Michael Klemm, Philippe Clauss, Michael Philippsen. A meta-predictor framework for prefetching in object-based DSMs. Concurrency and Computation: Practice and Experience, Wiley, 2009, 21 (14), pp.1789-1803. ⟨10.1002/cpe.1443⟩. ⟨inria-00504618⟩

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