Phylogenies (that is, tree-of-life relationships) derived from gene order data may prove crucial in answering some fundamental open questions in biomolecular evolution. Real-world interest is strong in determining these relationships. For example, pharmaceutical companies may use phylogeny reconstruction in drug discovery for finding plants with similar gene production. Health organizations study the evolution and spread of viruses such as HIV to gain understanding of future outbreaks. And governments are interested in aiding the production of foodstuffs like rice, wheat, and corn, by understanding the genetic code. Yet very few techniques are available for such phylogenetic reconstructions. Appropriate tools for analyzing such data may help resolve some difficult phylogenetic reconstruction problems; indeed, this new source of data has been embraced by many biologists in their phylogenetic work. With the rapid accumulation of whole genome sequences for a wide diversity of taxa, phylogenetic reconstruction based on changes in gene order and gene content is showing promise, particularly for resolving deep (i.e., old) branches. However, reconstruction from gene-order data is even more computationally intensive than reconstruction from sequence data, particularly in groups with large numbers of genes and highly rearranged genomes. We have developed a software suite, GRAPPA, that extends the breakpoint analysis (BPAnalysis) method of Sankoff and Blanchette while running much faster: in a recent analysis of a collection of chloroplast data for species of Campanulaceae on a 512-processor Linux supercluster with Myrinet, we achieved a one-million-fold speedup over BPAnalysis. GRAPPA currently can use either breakpoint or inversion distance (computed exactly) for its computati...

The ability to provide uniform shared-memory access to a significant number of processors in a single SMP node brings us much closer to the ideal PRAM parallel computer. In this paper, we develop new techniques for designing a uniform shared-memory algorithm from a PRAM algorithm and present the results of an extensive experimental study demonstrating that the resulting programs scale nearly linearly across a significant range of processors (from 1 to 64) and across the entire range of instance sizes tested. This linear speedup with the number of processors is, to our knowledge, the first ever attained in practice for intricate combinatorial problems. The example we present in detail here is a graph decomposition algorithm that also requires the computation of a spanning tree; this problem is not only of interest in its own right, but is representative of a large class of irregular combinatorial problems that have simple and efficient sequential implementations and fast PRAM algorithms, but have no known efficient parallel implementations. Our results thus offer promise for bridging the gap between the theory and practice of shared-memory parallel algorithms.

A phylogeny is the evolutionary history of a group of organisms; systematists (and other biologists) attempt to reconstruct this history from various forms of data about contemporary organisms. Phylogeny reconstruction is a crucial step in the understanding of evolution as well as an important tool in biological, pharmaceutical, and medical research. Phylogeny reconstruction from molecular data is very difficult: almost all optimization models give rise to NP-hard (and thus computationally intractable) problems. Yet approximations must be of very high quality in order to avoid outright biological nonsense. Thus many biologists have been willing to run farms of processors for many months in order to analyze just one dataset. High-performance algorithm engineering offers a battery of tools that can reduce, sometimes spectacularly, the running time of existing phylogenetic algorithms, as well as help designers produce better algorithms. We present an overview of algorithm engineering techniques, illustrating them with an application to the "breakpoint analysis" method of Sankoff et al., which resulted in the GRAPPA software suite. GRAPPA demonstrated a speedup in running time by over eight orders of magnitude over the original implementation on a variety of real and simulated datasets. We show how these algorithmic engineering techniques are directly applicable to a large variety of challenging combinatorial problems in computational biology.

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Due to fundamental physical limitations and power constraints, we are witnessing a radical change in commodity microprocessor architectures to multicore designs. Continued performance on multicore processors now requires the exploitation of concurrency at the algorithmic level. In this paper, we identify key issues in algorithm design for multicore processors and propose a computational model for these systems. We introduce SWARM (SoftWare and Algorithms for Running on Multi-core), a portable open-source parallel library of basic primitives that fully exploit multicore processors. Using this framework, we have implemented efficient parallel algorithms for important primitive operations such as prefixsums, pointer-jumping, symmetry breaking, and list ranking; for combinatorial problems such as sorting and selection; for parallel graph theoretic algorithms such as spanning tree, minimum spanning tree, graph decomposition, and tree contraction; and for computational genomics applications such as maximum parsimony. The main contributions of this paper are the design of the SWARM multicore framework, the presentation of a multicore algorithmic model, and validation results for this model. SWARM is freely available as open-source from