Graphics processing units (GPUs) are gaining widespread use in computationalchemistry and other scientific simulation contexts because of their hugeperformance advantages relative to conventional CPUs. However, the reliabilityof GPUs in error-intolerant applications is largely unproven. In particular, alack of error checking and correcting (ECC) capability in the memory subsystemsof graphics cards has been cited as a hindrance to the acceptance of GPUs ashigh-performance coprocessors, but the impact of this design has not beenpreviously quantified.In this article we present MemtestG80, our software for assessing memoryerror rates on NVIDIA G80 and GT200-architecture-based graphics cards.Furthermore, we present the results of a large-scale assessment of GPU errorrate, conducted by running MemtestG80 on over 20,000 hosts on the Folding@homedistributed computing network. Our control experiments on consumer-grade anddedicated-GPGPU hardware in a controlled environment found no errors. However,our survey over cards on Folding@home finds that, in their installedenvironments, two-thirds of tested GPUs exhibit a detectable, pattern-sensitiverate of memory soft errors. We demonstrate that these errors persist aftercontrolling for overclocking and environmental proxies for temperature, butdepend strongly on board architecture.<\/p>\n","protected":false},"excerpt":{"rendered":"
Graphics processing units (GPUs) are gaining widespread use in computationalchemistry and other scientific simulation contexts because of their hugeperformance advantages relative to conventional CPUs. However, the reliabilityof GPUs in error-intolerant applications is largely unproven. In particular, alack of error checking and correcting (ECC) capability in the memory subsystemsof graphics cards has been cited as a […]<\/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":[1782,14,633,20,183,409,176,202],"class_list":["post-1377","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-hardware-architecture","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-nvidia-geforce-9500-gt","tag-package","tag-tesla-c870"],"views":2134,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1377","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=1377"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1377\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1377"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1377"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1377"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}