{"id":9367,"date":"2013-05-11T22:24:45","date_gmt":"2013-05-11T19:24:45","guid":{"rendered":"http:\/\/hgpu.org\/?p=9367"},"modified":"2013-05-11T22:24:45","modified_gmt":"2013-05-11T19:24:45","slug":"a-distributed-cpu-gpu-framework-for-pairwise-alignments-on-large-scale-sequence-datasets","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9367","title":{"rendered":"A Distributed CPU-GPU Framework for Pairwise Alignments on Large-Scale Sequence Datasets"},"content":{"rendered":"<p>Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-Wunsch alignment algorithm. However, with the rapid growth of biological databases, performing all possible comparisons with this algorithm in serial becomes extremely time-consuming. The massive computational power of graphics processing units (GPUs) makes them an appealing choice for accelerating these computations. As such, CPU-GPU clusters can enable all-against-all comparisons on large datasets. In this paper, we present a hybrid MPI-CUDA framework for computing multiple pairwise sequence alignments on CPU-GPU clusters. Our design targets both homogeneous and heterogeneous clusters with nodes characterized by different hardware and computing capabilities. Our framework consists of the following components: a cluster-level dispatcher, a set of node-level dispatchers, and a set of CPU- and GPU-workers. The cluster-level dispatcher progressively distributes work to the compute nodes and aggregates the results. The node-level dispatchers distribute alignment tasks to available CPUs and GPUs and perform dual-buffering to hide data transfers between CPU and GPU. CPU- and GPU-workers perform pairwise sequence alignments using the Needleman-Wunsch algorithm. We propose and evaluate three designs for these GPU workers, all of them outperforming the existing open-source implementation from the Rodinia Benchmark Suite.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-Wunsch alignment algorithm. However, with the rapid growth of biological databases, performing all possible comparisons with this algorithm in serial becomes extremely time-consuming. The massive computational power of [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,89,3],"tags":[123,1781,231,14,106,452,242,20,1231,209,1390],"class_list":["post-9367","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-bioinformatics","tag-biology","tag-computational-biology","tag-cuda","tag-gpu-cluster","tag-heterogeneous-systems","tag-mpi","tag-nvidia","tag-nvidia-quadro-fx-2000","tag-sequence-alignment","tag-tesla-k20"],"views":2473,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9367","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9367"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9367\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9367"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9367"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}