Distributed caching strategy

Keum J. Kim; Eunice E. Santos; Eugene Santos Jr.

doi:10.1117/12.777881

15 April 2008 Distributed caching strategy

Keum J. Kim, Eunice E. Santos, Eugene Santos Jr.

Proceedings Volume 6961, Intelligent Computing: Theory and Applications VI; 69610D (2008) https://doi.org/10.1117/12.777881
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identifying the native conformation remains a computationally hard problem - even in simplified models such as hydrophobic-hydrophilic (HP) models. Distributed systems have become the focus of protein folding, providing high performance computing power to accommodate the conformation space. To use a distributed system efficiently (with limited resources), an appropriate strategy should be designed accordingly. Communication incurs overhead but can provide useful information in distributed systems through careful consideration. Our study focuses on understanding the behavior of distributed systems and developing an efficient communication strategy to save computational effort in order to obtain good solutions. In this paper, we propose a distributed caching strategy, which reuses partial results of computations and transmits the cached and reusable information among neighboring inter-connected processors. In order to validate this idea in a practical setting, we present algorithms to retrieve and restore the cached information and apply them to 2D triangular HP lattice models through coarse-grained parallel genetic algorithms (CPGAs). Our experimental results demonstrate the time savings as well as the limits in caching improvements for our distributed caching strategy.

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Keum J. Kim, Eunice E. Santos, and Eugene Santos Jr. "Distributed caching strategy", Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 69610D (15 April 2008); https://doi.org/10.1117/12.777881

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KEYWORDS

Proteins

Distributed computing

Evolutionary algorithms

Systems modeling

Genetic algorithms

Telecommunications

Algorithm development

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