{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/44241"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/44241","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"A distributed CPU-GPU framework for large-scale pairwise alignment","abstract":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] 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. This thesis presents a hybrid MPI-CUDA framework for computing multiple pairwise sequence alignments on CPU-GPU clusters. The design targets both homogeneous and heterogeneous clusters with nodes characterized by different hardware and computing capabilities. The framework consists of the following components: a cluster-level dispatcher, a set of node-level dispatchers, and a set of CPU- and GPUworkers. 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. The proposed GPU workers are evaluated on different platforms and all of them outperform the existing open-source implementation from the Rodinia Benchmark Suite.","abstract_html":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] 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. This thesis presents a hybrid MPI-CUDA framework for computing multiple pairwise sequence alignments on CPU-GPU clusters. The design targets both homogeneous and heterogeneous clusters with nodes characterized by different hardware and computing capabilities. The framework consists of the following components: a cluster-level dispatcher, a set of node-level dispatchers, and a set of CPU- and GPUworkers. 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. The proposed GPU workers are evaluated on different platforms and all of them outperform the existing open-source implementation from the Rodinia Benchmark Suite.","abstract_has_math":false,"creators":["Li, Da"],"institution":"University of Missouri--Columbia","degree_name":"M.S.","degree_level":"Masters","degree_discipline":"Computer engineering (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Becchi, Michela"],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014","date_published":"2014","updated_at":"2026-07-24T03:09:05Z","subjects":["Author supplied: heterogenoous cluster, GPU, needleman-wunsch, CUDA"],"languages":["eng","English"],"rights":["Access to files is limited to the University of Missouri--Columbia with SSO login."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/44241","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Becchi, Michela"]},{"key":"dc:creator","label":"Author","values":["Li, Da"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-11-13T16:27:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-11-13T16:27:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2014"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer engineering (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Columbia"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Author supplied: heterogenoous cluster, GPU, needleman-wunsch, CUDA"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Access to files is limited to the University of Missouri--Columbia with SSO login."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/44241"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] 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. This thesis presents a hybrid MPI-CUDA framework for computing multiple pairwise sequence alignments on CPU-GPU clusters. The design targets both homogeneous and heterogeneous clusters with nodes characterized by different hardware and computing capabilities. The framework consists of the following components: a cluster-level dispatcher, a set of node-level dispatchers, and a set of CPU- and GPUworkers. 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. The proposed GPU workers are evaluated on different platforms and all of them outperform the existing open-source implementation from the Rodinia Benchmark Suite."]},{"key":"dc:title","label":"Title","values":["A distributed CPU-GPU framework for large-scale pairwise alignment"]}]}],"canonical_facts":{"dc:contributor.advisor":["Becchi, Michela"],"dc:creator":["Li, Da"],"dc:date.accessioned":["2014-11-13T16:27:48Z"],"dc:date.available":["2014-11-13T16:27:48Z"],"dc:date.issued":["2014"],"dc:description.abstract":["[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] 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. This thesis presents a hybrid MPI-CUDA framework for computing multiple pairwise sequence alignments on CPU-GPU clusters. The design targets both homogeneous and heterogeneous clusters with nodes characterized by different hardware and computing capabilities. The framework consists of the following components: a cluster-level dispatcher, a set of node-level dispatchers, and a set of CPU- and GPUworkers. 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. The proposed GPU workers are evaluated on different platforms and all of them outperform the existing open-source implementation from the Rodinia Benchmark Suite."],"dc:identifier.uri":["https://hdl.handle.net/10355/44241"],"dc:language":["English"],"dc:language.iso":["eng"],"dc:publisher":["University of Missouri--Columbia"],"dc:rights":["Access to files is limited to the University of Missouri--Columbia with SSO login."],"dc:subject":["Author supplied: heterogenoous cluster, GPU, needleman-wunsch, CUDA"],"dc:title":["A distributed CPU-GPU framework for large-scale pairwise alignment"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer engineering (MU)"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Missouri--Columbia"]},"updated_at":"2026-07-24T03:09:05Z"}