On the Parallelization of the SProt Measure and the TM-Score Algorithm
Similarity measures for the protein structures are quite complex and require significant computational time. We propose a parallel approach to this problem to fully exploit the computational power of current CPU architectures. This paper summarizes experience and insights acquired from the parallel implementation of the SProt similarity method, its database access method, and also the wellknown TM-score algorithm. The implementation scales almost linearly with the number of CPUs and achieves 21.4× speedup on a 24-core system. The implementation is currently employed in the web application http://siret.cz/p3s.
Keywordsprotein structure similarity parallel optimization
Unable to display preview. Download preview PDF.
- 3.Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. The EMBO Journal 5(4), 823–826 (1986)Google Scholar
- 5.Tung, C.H.H., Huang, J.W.W., Yang, J.M.M.: Kappa-alpha plot derived structural alphabet and BLOSUM-like substitution matrix for rapid search of protein structure database. Genome Biol. 8(3), R31 (2007)Google Scholar
- 7.Galgonek, J., Hoksza, D., Skopal, T.: SProt: sphere-based protein structure similarity algorithm. BMC Proteome Science 9(suppl. 1), S20 (2011)Google Scholar
- 14.Amdahl, G.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18-20, 1967, Spring Joint Computer Conference, pp. 483–485. ACM (1967)Google Scholar
- 15.Reinders, J.: Intel threading building blocks: outfitting C++ for multi-core processor parallelism. O’Reilly Media, Inc. (2007)Google Scholar