CPU-GPU clusters have emerged as a dominant HPC platform, with the three of the four fastest supercomputers in the world falling in this category. The reasons for the popularity of these environments include their cost-effectiveness and energy efficiency. The need for exploiting both the CPU and GPU on each node of such platforms has created a renewed interest in heterogeneous computing [14]. Implementation of such a heterogeneous system on a cluster is a challenge. At the same time, FREERIDE – a map-reduce like framework can be used efficiently to develop data-intensive applications on clusters and multi-core systems, because of its simplicity and robustness. In this thesis, we are developing a heterogeneous implementation on a CPU-GPU cluster for a Monte Carlo Simulation application using FREERIDE – a map-reduce like framework based on the generalized reduction. We show through experiments, the support for enabling scalable and efficient implementation of data-intensive applications in a heterogeneous cluster of many-core GPUs and CPUs. Our contributions are 2 fold: 1) develop heterogeneous version of Monte Carlo application for distributed environment using FREERIDE APIs; 2) We present a new approach of load balancing between a CPU and a GPU on a node to better utilize the computing power of CPUs and\/or GPUs. We evaluate our heterogeneous implementation on a cluster. We show an almost linear speedup on this cluster over execution with 1 CPU core, 1 GPU core and a combination of 1 CPU and 1 GPU cores respectively. Our application also achieve an improvement of 20% by using CPUs and GPUs simultaneously, over the best performance achieved from using only one of the types of resources in the cluster using the new load balancing technique.<\/p>\n","protected":false},"excerpt":{"rendered":"
CPU-GPU clusters have emerged as a dominant HPC platform, with the three of the four fastest supercomputers in the world falling in this category. The reasons for the popularity of these environments include their cost-effectiveness and energy efficiency. The need for exploiting both the CPU and GPU on each node of such platforms has created […]<\/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":[1782,14,106,452,72,20,224,390],"class_list":["post-6387","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-monte-carlo-simulation","tag-nvidia","tag-nvidia-quadro-fx-5600","tag-thesis"],"views":2207,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6387","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=6387"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6387\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6387"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6387"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6387"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}