Dynamic programming with convexity, concavity and sparsity
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
A hybrid architecture for bioinformatics
Future Generation Computer Systems - Parallel computing technologies (PaCT-2001)
Massively Parallel Solutions for Molecular Sequence Analysis
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
An FPGA-Based Coprocessor for the Parsing of Context-Free Grammars
FCCM '00 Proceedings of the 2000 IEEE Symposium on Field-Programmable Custom Computing Machines
Optimal evolutionary tree comparison by sparse dynamic programming
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
A parallel wavefront algorithm for efficient biological sequence comparison
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
A parallel BSP algorithm for irregular dynamic programming
APPT'07 Proceedings of the 7th international conference on Advanced parallel processing technologies
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Dynamic programming is a widely applied algorithm design technique in many areas such as computational biology and scientific computing Typical applications using this technique are compute-intensive and suffer from long runtimes on sequential architectures Therefore, many parallel algorithms for both fine-grained and coarse-grained architectures have been introduced However, the commonly used data partitioning scheme can not be efficiently applied to irregular dynamic programming applications, i.e dynamic programming applications with an uneven computational load density In this paper we present an efficient coarse-grained parallel algorithm for such kind of applications This new algorithm can balance the load among processors using a tunable block-cyclic data partitioning scheme We present a theoretical analysis and experimentally show that it leads to significant runtime savings for several irregular dynamic programming applications on PC clusters.