Several biologically inspired applications have been motivated by Spiking Neural Networks (SNNs) such as the Hodgkin-Huxley (HH) and Izhikevich models, owing to their high biological accuracy. The inherent massively parallel nature of the SNN simulations makes them a good fit for heterogeneous computing resources such as the General Purpose Graphical Processing Unit (GPGPU) clusters. In this research, we explore multi-level parallelism offered by heterogeneous computing resources for largescale SNN simulations. These simulations were performed using a two-level character recognition network based on the aforementioned SNN models on NCSA’s Forge GPGPU cluster. Our multi-node GPGPU implementation distributes the computations to either CPU or GPGPU based on task classification and utilizes all the available multi-level parallelism offered to ensure maximum heterogeneous resource utilization. Our multinode GPGPU implementation scales up to 200 million neurons for the two-level network and achieves a speedup of 355x over an equivalent MPI-only implementation.<\/p>\n","protected":false},"excerpt":{"rendered":"
Several biologically inspired applications have been motivated by Spiking Neural Networks (SNNs) such as the Hodgkin-Huxley (HH) and Izhikevich models, owing to their high biological accuracy. The inherent massively parallel nature of the SNN simulations makes them a good fit for heterogeneous computing resources such as the General Purpose Graphical Processing Unit (GPGPU) clusters. In […]<\/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":[11,89,3],"tags":[1782,14,106,452,242,34,20,1017],"class_list":["post-8257","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-gpu-cluster","tag-heterogeneous-systems","tag-mpi","tag-neural-networks","tag-nvidia","tag-tesla-m2070"],"views":2298,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8257","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=8257"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8257\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8257"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8257"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8257"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}