{"id":11299,"date":"2014-01-26T23:47:58","date_gmt":"2014-01-26T21:47:58","guid":{"rendered":"http:\/\/hgpu.org\/?p=11299"},"modified":"2014-01-26T23:47:58","modified_gmt":"2014-01-26T21:47:58","slug":"gem5-gpu-a-heterogeneous-cpu-gpu-simulator","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11299","title":{"rendered":"gem5-gpu: A Heterogeneous CPU-GPU Simulator"},"content":{"rendered":"<p>gem5-gpu is a new simulator that models tightly integrated CPU-GPU systems. It builds on gem5, a modular fullsystem CPU simulator, and GPGPU-Sim, a detailed GPGPU simulator. gem5-gpu routes most memory accesses through Ruby, which is a highly configurable memory system in gem5. By doing this, it is able to simulate many system configurations, ranging from a system with coherent caches and a single virtual address space across the CPU and GPU to a system that maintains separate GPU and CPU physical address spaces. gem5-gpu can run most unmodified CUDA 3.2 source code. Applications can launch non-blocking kernels, allowing the CPU and GPU to execute simultaneously.We present gem5-gpu&#8217;s software architecture and a brief performance validation. We also discuss possible extensions to the simulator. gem5-gpu is open source and available at gem5-gpu.cs.wisc.edu.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>gem5-gpu is a new simulator that models tightly integrated CPU-GPU systems. It builds on gem5, a modular fullsystem CPU simulator, and GPGPU-Sim, a detailed GPGPU simulator. gem5-gpu routes most memory accesses through Ruby, which is a highly configurable memory system in gem5. By doing this, it is able to simulate many system configurations, ranging from [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,1398,452,20,974],"class_list":["post-11299","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-gpgpu-sim","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-580"],"views":3596,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11299","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=11299"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11299\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11299"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11299"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11299"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}