{"id":2269,"date":"2010-12-29T12:58:00","date_gmt":"2010-12-29T12:58:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=2269"},"modified":"2010-12-29T12:58:00","modified_gmt":"2010-12-29T12:58:00","slug":"single-particle-3d-reconstruction-from-cryo-electron-microscopy-images-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2269","title":{"rendered":"Single-particle 3D reconstruction from cryo-electron microscopy images on GPU"},"content":{"rendered":"<p>Single-particle 3D reconstruction from cryo-electron microscopy (cryo-EM) images is a kernel application of biological molecules analysis, as the computational requirement of which is now beyond PetaFlop for a high-resolution 3D structure. In this paper, we quantitatively analyze the workload, computational intensity and memory performance of the application, parallelize it on an emerging multicore architecture GPU-CUDA. Further we apply a percolation technique to decouple computation with memory operations and orchestrate thread-data mapping to reduce the overhead off-chip memory operations. Finally we tested our optimization strategy on a popular open-source package EMAN to GPU-CUDA, which achieves a relative speedup of about 10X to the original CPU-only EMAN. The experimental results also show that the proposed percolation programming greatly improves utilization of memory bandwidth and floating-point units.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Single-particle 3D reconstruction from cryo-electron microscopy (cryo-EM) images is a kernel application of biological molecules analysis, as the computational requirement of which is now beyond PetaFlop for a high-resolution 3D structure. In this paper, we quantitatively analyze the workload, computational intensity and memory performance of the application, parallelize it on an emerging multicore architecture GPU-CUDA. [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":[89,33,38,3],"tags":[14,586,1786,512,1788,20,183],"class_list":["post-2269","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-medicine","category-paper","tag-cuda","tag-electron-microscopy","tag-image-processing","tag-image-reconstruction","tag-medicine","tag-nvidia","tag-nvidia-geforce-8800-gtx"],"views":2490,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2269","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=2269"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2269\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2269"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2269"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2269"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}