{"id":11355,"date":"2014-02-05T01:06:07","date_gmt":"2014-02-04T23:06:07","guid":{"rendered":"http:\/\/hgpu.org\/?p=11355"},"modified":"2014-02-05T01:06:07","modified_gmt":"2014-02-04T23:06:07","slug":"bone-structure-analysis-on-multiple-gpgpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11355","title":{"rendered":"Bone structure analysis on multiple GPGPUs"},"content":{"rendered":"<p>Osteoporosis is a disease that affects a growing number of people by increasing the fragility of their bones. To improve the understanding of the bone quality, large scale computer simulations are applied. A fast, scalable and memory efficient solver for such problems is ParOSol. It uses the preconditioned conjugate gradient algorithm with a multigrid preconditioner. A modification of ParOSol is presented that profits from the exorbitant compute capabilities of recent general-purpose graphics processing units (GPGPUs). Adaptations of data structures for the GPGPU are discussed. The fastest implementation on a GPGPU achieves a speedup of more than five compared with the CPU implementation and scales from 1 to at least 256 GPGPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Osteoporosis is a disease that affects a growing number of people by increasing the fragility of their bones. To improve the understanding of the bone quality, large scale computer simulations are applied. A fast, scalable and memory efficient solver for such problems is ParOSol. It uses the preconditioned conjugate gradient algorithm with a multigrid preconditioner. [&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":[89,38,3],"tags":[14,1788,20,1390],"class_list":["post-11355","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-cuda","tag-medicine","tag-nvidia","tag-tesla-k20"],"views":2042,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11355","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=11355"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11355\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11355"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11355"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11355"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}