Journal of VLSI Signal Processing 48, 223–238, 2007 * 2007 Springer Science + Business Media, LLC. Manufactured in The United States. DOI: 10.1007/s11265-007-0062-9 MPI-HMMER-Boost: Distributed FPGA Acceleration JOHN PAUL WALTERS Institute for Scientific Computing, Wayne State University, Detroit, MI 48202, USA XIANDONG MENG Electrical and Computer Engineering Department, Wayne State University, Detroit, MI 48202, USA VIPIN CHAUDHARY Department of Computer Science and Engineering University at Buffalo, The State University of New York, Buffalo, NY 14260, USA TIM OLIVER, LEOW YUAN YEOW AND DARRAN NATHAN Progeniq Pte Ltd., 8 Prince George_s Park, 118407, Singapore, Singapore BERTIL SCHMIDT UNSW Asia, 1 Kay Siang Road, 248922, Queenstown, Singapore JOSEPH LANDMAN Scalable Informatics LLC, 2433 Woodmont, Canton, MI 48188, USA Received: 15 December 2006; Revised: 5 March 2007; Accepted: 17 March 2007 Abstract. HMMER, based on the profile Hidden Markov Model (HMM) is one of the most widely used sequence database searching tools, allowing researchers to compare HMMs to sequence databases or sequences to HMM databases. Such searches often take many hours and consume a great number of CPU cycles on modern computers. We present a cluster-enabled hardware/software-accelerated implementation of the HMMER search tool hmmsearch. Our results show that combining the parallel efficiency of a cluster with one or more high-speed hardware accelerators (FPGAs) can significantly improve performance for even the most time consuming searches, often reducing search times from several hours to minutes. Keywords: HMMER, database searching, FPGA, VLSI, MPI, profile hidden markov models 1. Introduction Protein sequence analysis tools to predict homolo- gy, structure and function of particular peptide sequences exist in abundance. One of the most commonly used tools is the profile hidden Markov model algorithm developed by Eddy [9] and John Paul Walters: This research was supported in part by NSF IGERT grant 9987598 and the Institute for Scientific Computing at Wayne State University.Vipin Chaudhary: This research was supported in part by NSF IGERT grant 9987598, the Institute for Scientific Computing at Wayne State University, MEDC/Michigan Life Science Corridor, and NYSTAR.