{"id":11568,"date":"2014-03-10T22:44:53","date_gmt":"2014-03-10T20:44:53","guid":{"rendered":"http:\/\/hgpu.org\/?p=11568"},"modified":"2014-03-10T22:44:53","modified_gmt":"2014-03-10T20:44:53","slug":"massively-parallel-read-mapping-on-gpus-with-peanut","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11568","title":{"rendered":"Massively parallel read mapping on GPUs with PEANUT"},"content":{"rendered":"<p>We present PEANUT (ParallEl AligNment UTility), a highly parallel GPU-based read mapper with several distinguishing features, including a novel q-gram index (called the q-group index) with small memory footprint built on-the-fly over the reads and the possibility to output both the best hits or all hits of a read. Designing the algorithm particularly for the GPU architecture, we were able to reach maximum core occupancy for several key steps. Our benchmarks show that PEANUT outperforms other state-of- the-art mappers in terms of speed and sensitivity. The software is available at http:\/\/peanut.readthedocs.org\/.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present PEANUT (ParallEl AligNment UTility), a highly parallel GPU-based read mapper with several distinguishing features, including a novel q-gram index (called the q-group index) with small memory footprint built on-the-fly over the reads and the possibility to output both the best hits or all hits of a read. Designing the algorithm particularly for the [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,90,3],"tags":[1782,94,20,974,1504,1793,176,513],"class_list":["post-11568","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-data-structures-and-algorithms","tag-nvidia","tag-nvidia-geforce-gtx-580","tag-nvidia-geforce-gtx-780","tag-opencl","tag-package","tag-python"],"views":2656,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11568","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=11568"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11568\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11568"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11568"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}