High-Performance Algorithm Engineering for Computational Phylogenetics

High-Performance Algorithm Engineering for Computational Phylogenetics (2002)

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by Bernard M. E. Moret , David A. Bader , Tandy Warnow

Venue:	J. Supercomputing
Citations:	19 - 6 self

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Datum	Value	Source
TITLE	High-Performance Algorithm Engineering for Computational Phylogenetics	user correction - Legacy Corrections
AUTHOR NAME	Bernard M. E. Moret	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science, University of New Mexico, Albuquerque	user correction - Legacy Corrections
AUTHOR ADDR	NM 87131	user correction - Legacy Corrections
AUTHOR NAME	David A. Bader	user correction - Legacy Corrections
AUTHOR AFFIL	; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque; Department of Computer Sciences, University of Texas, Austin	user correction - Legacy Corrections
AUTHOR ADDR	; NM 87131; TX 78712	user correction - Legacy Corrections
AUTHOR NAME	Tandy Warnow	user correction - Legacy Corrections
ABSTRACT	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.	user correction - Legacy Corrections
YEAR	2002	user correction - Legacy Corrections
VENUE	J. Supercomputing	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	99--111	INFERENCE
VOLUME	22	INFERENCE
CITATIONS	46 found	ParsCit 1.0