By Topic

Parallel, scalable, memory-efficient backtracking for combinatoria modeling of large-scale biological systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Byung-Hoon Park ; Comput. Sci. & Math. Div., Oak Ridge Nat. Lab., Oak Ridge, TN ; Schmidt, M. ; Thomas, K. ; Karpinets, T.
more authors

Data-driven modeling of biological systems such as protein- protein interaction networks is data-intensive and combinatorially challenging. Backtracking can constrain a combinatorial search space. Yet, its recursive nature, exacerbated by data-intensity, limits its applicability for large-scale systems. Parallel, scalable, and memory-efficient backtracking is a promising approach. Parallel backtracking suffers from unbalanced loads. Load rebalancing via synchronization and data movement is prohibitively expensive. Balancing these discrepancies, while minimizing end-to-end execution time and memory requirements, is desirable. This paper introduces such a framework. Its scalability and efficiency, demonstrated on the maximal clique enumeration problem, are attributed to the proposed: (a) representation of search tree decomposition to enable parallelization; (b) depth-first parallel search to minimize memory requirement; (c) least stringent synchronization to minimize data movement; and (d) on-demand work stealing with stack splitting to minimize processors' idle time. The applications of this framework to real biological problems related to bioethanol production are discussed.

Published in:

Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on

Date of Conference:

14-18 April 2008