Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex models and manage massive data volumes, model processes across interdisciplinary boundaries, and transform business and engineering practices. Increasingly, cyberinfrastructure including petascale computers is required to address our national and global priorities, such as sustainability of our natural environment by reducing our carbon footprint and by decreasing our dependencies on fossil fuels, improving human health and living conditions, understanding the mechanisms of life from molecules and systems to organisms and populations, preventing the spread of disease, predicting and tracking severe weather, recovering from natural and human-caused disasters, maintaining national security, and mastering nanotechnologies. Several of our most fundamental intellectual questions also require computation, such as the formation of the universe, the the evolution of life, and the properties of matter. In this talk, we focus on the grand challenge to reconstruct the tree of life. Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for the most part) computationally expensive. High-performance algorithm engineering offers a battery of tools that can reduce, sometimes spectacularly, the running time of existing approaches. We discuss one such such application, in which we started with the method known as breakpoint analysis (developed by Sankoff and his colleagues) and produced a software suite, GRAPPA, that demonstrated over a billion-fold speedup in running time (on a variety of real and simulated datasets), by combining low-level algorithmic improvements, cache-aware programming, careful performance tuning, and massive parallelism. The phylogeny reconstruction now can be performed in parallel and attain a linear speedup with the number of processors. We show how these techniques are directly applicable to a large variety of problems in computational biology.
David A. Bader is Executive Director of High-Performance Computing and a Professor in Computational Science and Engineering, a division within the College of Computing, at Georgia Institute of Technology. Dr. Bader also serves as Director of the Sony-Toshiba-IBM Center of Competence for the Cell Broadband Engine Processor located at Georgia Tech. He received his Ph.D. in 1996 from The University of Maryland, was awarded a National Science Foundation (NSF) Postdoctoral Research Associateship in Experimental Computer Science. He is an NSF CAREER Award recipient, an investigator on several NSF awards, was a distinguished speaker in the IEEE Computer Society Distinguished Visitors Program, and a member of the IBM PERCS team for the DARPA High Productivity Computing Systems program. Dr. Bader serves on the Research Advisory Council of Internet2 and the Steering Committees of the IPDPS and HiPC conferences, and was the General co-Chair for IPDPS (2004–2005), and Vice General Chair for HiPC (2002–2004). David has chaired several major conference program committees: Program Chair for HiPC 2005, Program Vice-Chair for IPDPS 2006 and Program Vice-Chair for ICPP 2006, and has served on numerous conference program committees related to parallel processing and computational science & engineering, is an associate editor for several high impact publications including the IEEE Transactions on Parallel and Distributed Systems (TPDS), the ACM Journal of Experimental Algorithmics (JEA), IEEE DSOnline, and Parallel Computing, is a Senior Member of the IEEE Computer Society and a Member of the ACM. Dr. Bader has been a pioneer in the field of high-performance computing for problems in bioinformatics and computational genomics. He has co-chaired a series of meetings, the IEEE International Workshop on High-Performance Computational Biology (HiCOMB), written several book chapters, and co-edited special issues of the Journal of Parallel and Distributed Computing (JPDC) and IEEE TPDS on high-performance computational biology. He has co-authored over 90 articles in peer-reviewed journals and conferences, and his main areas of research are in parallel algorithms, combinatorial optimization, and computational biology and genomics.