Adaptive technique for automatic communication access pattern discovery applied to data prefetching in distributed applications using neural networks and stochastic models

Authors:
Evgueni Dodonov;Rodrigo Fernandes de Mello;Laurence Tianruo Yang
Affiliations:
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, SP, Brazil;Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, SP, Brazil;St. Francis Xavier University, Antigonish, NS, Canada
Venue:
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
Year:
2006

Citing 10
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Abstract

The distributed computing performance is usually limited by the data transfer rate and access latency. Techniques such as data caching and prefetching were developed to overcome this limitation. However, such techniques require the knowledge of application behavior in order to be effective. In this sense, we propose new application communication behavior discovery techniques that, by classifying and analyzing application access patterns, is able to predict future application data accesses. The proposed techniques use stochastic methods for application state change prediction and neural networks for access pattern discovery based on execution history, and is evaluated using the NAS Parallel Benchmark suite.