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Integrating parallelization strategies for linkage analysis.

S K Gupta·A A Schäffer·A L Cox·S Dwarkadas·W Zwaenepoel
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Abstract

We describe a second parallel implementation of the ILINK program from the LINKAGE package that improves on our previous implementation. To improve running time we integrated the strategy of parallel estimation of the gradient at a candidate recombination fraction vector with a previously implemented strategy for evaluating in parallel the likelihood at one vector. We also integrated an adaptive loadbalancing strategy in conjunction with our previous static loadbalancing strategy. We implemented a strategy for partitioning input pedigrees, but this slowed down the program; we give some evidence for what the problems are. To best exploit parallelism at all levels of the program and to take advantage of both coarse-grain and fine-grain parallelism it is necessary to combine multiple algorithmic strategies.

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AlgorithmsEvaluation Studies as TopicGenetic LinkageGenetic TechniquesHumansLikelihood FunctionsSoftwareComparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.

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S K Gupta, A A Schäffer, A L Cox, S Dwarkadas, W Zwaenepoel. 1995. Integrating parallelization strategies for linkage analysis.. https://doi.org/10.1006/cbmr.1995.1009

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