A Scalable Parallel Algorithm for Large-Scale Protein Sequence Homology Detection

Changjun Wu, Ananth Kalyanaraman, William R. Cannon
2010 2010 39th International Conference on Parallel Processing  
Protein sequence homology detection is a fundamental problem in computational molecular biology, with a pervasive application in nearly all analyses that aim to structurally and functionally characterize protein molecules. While detecting homology between two protein sequences is computationally inexpensive, detecting pairwise homology at a large-scale becomes prohibitive, requiring millions of CPU hours. Yet, there is currently no efficient method available to parallelize this kernel. In this
more » ... aper, we present the key characteristics that make this problem particularly hard to parallelize, and then propose a new parallel algorithm that is suited for large-scale protein sequence data. Our method, called pGraph, is designed using a hierarchical multiple-master multiple-worker model, where the processor space is partitioned into subgroups and the hierarchy helps in ensuring the workload is load balanced fashion despite the inherent irregularity that may originate in the input. Experimental evaluation demonstrates that our method scales linearly on all input sizes tested (up to 640K sequences) on a 1,024 node supercomputer. In addition to demonstrating strong scaling, we present an extensive study of the various components of the system and related parametric studies. Keywords-Parallel protein sequence homology detection; parallel sequence graph construction; hierarchical master-worker paradigm.
doi:10.1109/icpp.2010.41 dblp:conf/icpp/WuKC10 fatcat:xzkv6d276veixcpgnjrw7icyhi