{"id":8480,"date":"2012-11-10T22:20:12","date_gmt":"2012-11-10T20:20:12","guid":{"rendered":"http:\/\/hgpu.org\/?p=8480"},"modified":"2012-11-10T22:20:12","modified_gmt":"2012-11-10T20:20:12","slug":"parallel-execution-of-a-parameter-sweep-for-molecular-dynamics-simulations-in-a-hybrid-gpucpu-environment","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8480","title":{"rendered":"Parallel execution of a parameter sweep for molecular dynamics simulations in a hybrid GPU\/CPU environment"},"content":{"rendered":"<p>Molecular Dynamics (MD) simulations can help to utimingnderstand an immense number of phenomena at the nano and microscale. They often require the exploration of large parameter space, and a possible parallelization strategy consists of sending different parameter sets to different processors. Here we present such approach using a hybrid environment of Graphic Processing Units (GPUs) and CPU cores. We take advantage of the software LAMMPS (lammps.sandia.gov), which is already prepared to run in a hybrid environment, in order to do an efficient parameter sweep. One example is presented in this work: the collision of two clusters is sampled over a multivariate space to obtain information on the resulting structural properties.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Molecular Dynamics (MD) simulations can help to utimingnderstand an immense number of phenomena at the nano and microscale. They often require the exploration of large parameter space, and a possible parallelization strategy consists of sending different parameter sets to different processors. Here we present such approach using a hybrid environment of Graphic Processing Units (GPUs) [&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":[66,89,3],"tags":[1790,14,112,20,680,176,1380,378],"class_list":["post-8480","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-nvidia-cuda","category-paper","tag-chemistry","tag-cuda","tag-molecular-dynamics","tag-nvidia","tag-openmpi","tag-package","tag-ruby","tag-tesla-c2050"],"views":3490,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8480","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=8480"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8480\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8480"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8480"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8480"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}