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In this paper, we present a two-layer hierarchical parallel implementation of gene differential association analysis. It takes advantage of both fine- and coarse-grain (with granularity defined by the frequency of communication) parallelism in order to effectively leverage the non-uniform nature of parallel processing available in the cutting-edge systems of today.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Our results show that this hierarchical strategy matches data sharing behavior to the properties of the underlying hardware, thereby reducing the memory and bandwidth needs of the application. The resulting improved efficiency reduces computation time and allows the gene differential association analysis code to scale its execution with the number of processors. The code and biological data used in this study are downloadable from <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.urmc.rochester.edu\/biostat\/people\/faculty\/hu.cfm\" ext-link-type=\"uri\">http:\/\/www.urmc.rochester.edu\/biostat\/people\/faculty\/hu.cfm<\/jats:ext-link>\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The performance sweet spot occurs when using a number of threads per MPI process that allows the working sets of the corresponding MPI processes running on the multicore to fit within the machine cache. Hence, we suggest that practitioners follow this principle in selecting the appropriate number of MPI processes and threads within each MPI process for their cluster configurations. We believe that the principles of this hierarchical approach to parallelization can be utilized in the parallelization of other computationally demanding kernels.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-12-374","type":"journal-article","created":{"date-parts":[[2011,9,21]],"date-time":"2011-09-21T18:32:56Z","timestamp":1316629976000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hierarchical Parallelization of Gene Differential Association Analysis"],"prefix":"10.1186","volume":"12","author":[{"given":"Mark","family":"Needham","sequence":"first","affiliation":[]},{"given":"Rui","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Sandhya","family":"Dwarkadas","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Qiu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,9,21]]},"reference":[{"key":"4835_CR1","first-page":"Article7","volume":"5","author":"L Klebanov","year":"2006","unstructured":"Klebanov L, Jordan C, Yakovlev A: A new type of stochastic dependence revealed in gene expression data. 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