{"id":6419,"date":"2011-11-28T18:05:41","date_gmt":"2011-11-28T16:05:41","guid":{"rendered":"http:\/\/hgpu.org\/?p=6419"},"modified":"2011-11-28T18:05:41","modified_gmt":"2011-11-28T16:05:41","slug":"a-hybrid-parallel-framework-for-computational-solid-mechanics","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6419","title":{"rendered":"A hybrid parallel framework for computational solid mechanics"},"content":{"rendered":"<p>A novel, hybrid parallel C++ framework for computational solid mechanics is developed and presented. The modular and extensible design of this framework allows it to support a wide variety of numerical schemes including discontinuous Galerkin formulations and higher order methods, multiphysics problems, hybrid meshes made of different types of elements and a number of different linear and non-linear solvers. In addition, native, seamless support is included for hardware acceleration by Graphics Processing Units (GPUs) via NVIDIA&#8217;s CUDA architecture for both single GPU workstations and heterogenous clusters of GPUs. The capabilities of the framework are demonstrated through a series of sample problems, including a laser induced cylindrical shock propagation, a dynamic problem involving a micro-truss array made of millions of elements, and a tension problem involving a shape memory alloy with a multifield formulation to model the superelastic effect.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A novel, hybrid parallel C++ framework for computational solid mechanics is developed and presented. The modular and extensible design of this framework allows it to support a wide variety of numerical schemes including discontinuous Galerkin formulations and higher order methods, multiphysics problems, hybrid meshes made of different types of elements and a number of different [&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":[89,3,12],"tags":[14,555,242,20,1783,199,390],"class_list":["post-6419","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-cuda","tag-hybrid-computing","tag-mpi","tag-nvidia","tag-physics","tag-tesla-c1060","tag-thesis"],"views":1861,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6419","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=6419"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6419\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6419"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6419"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6419"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}