Highlights of the BioTM 2010 workshop on advances in bio text mining

Simulations of statistical models have been used to validate theories of past events in evolution of species. Studies concerning human evolution are important for understanding about our history and biodiversity. However, these approaches use complex statistical models, leading to high computational cost. The present paper proposes optimization techniques for Hyper-threaded multicore architectures to improve the computational performance of these simulations. Combining granularity studies and Hyper-threading optimization, we improved the performance of simulations in more than 30%, if compared with common parallel execution (default parallelization applied by users). The performance was evaluated using a complex example of human evolution studies [1]. For this example, our techniques enable the user to decrease the simulation execution time from 50 days (sequential runtime) to less than 5 days. In addition, the evaluation has been extended for simulations running on multiple multicore cluster nodes. Our measurements show a high Speed-up, close to theoretical maximum, being 129 times faster for 160 computational cores. This represents an efficiency of 81%.

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RetroMine, or how to provide in-depth retrospective studies from Medline in a glance: the hepcidin use-case

Cadeville¹,

Loréal²,

Moussouni-Marzolf³

2015

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Highlights of the BioTM 2010 workshop on advances in bio text mining

Cited by 3 publications

References 15 publications

QTLMiner: QTL database curation by mining tables in literature

QTLMiner: QTL database curation by mining tables in literature

Optimizing the Execution of Statistical Simulations for Human Evolution in Hyper-threaded Multicore Architectures

RetroMine, or how to provide in-depth retrospective studies from Medline in a glance: the hepcidin use-case

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