Two decades of microprocessor architecture driven by quantitative 90\/10 optimization has delivered an extraordinary 1000-fold improvement in microprocessor performance, enabled by transistor scaling which improved density, speed, and energy. Recent generations of technology have produced limited benefits in transistor speed and power, so as a result the industry has turned to multicore parallelism for performance scaling . Long-range studies [1, 2] indicate that radical approaches are needed in the coming decade – extreme parallelism, near-threshold voltage scaling (and resulting poor single-thread performance), and tolerance of extreme variability – are required to maximize energy efficiency and compute density. These changes create major new challenges in architecture and software. As a result, the performance and energy-efficiency advantages of heterogeneous architectures are increasingly attractive. However, computing has lacked an optimization paradigm in which to systematically analyze, assess, and implement heterogeneous computing. We propose a new paradigm, "10×10", which clusters applications into a set of less frequent cases (ie. 10% cases), and creates customized architecture, implementation, and software solutions for each of these clusters, achieving significantly better energy efficiency and performance. We call this approach "10×10" because the approach is exemplified by optimizing ten different 10% cases, reflecting a shift away from the 90\/10 optimization paradigm framed by Amdahl’s law [3]. We describe the 10×10 approach, explain how it solves the major obstacles to widespread adoption of heterogeneous architectures, and present a preliminary 10×10 clustering, strawman architecture, and software tool chain approach.<\/p>\n","protected":false},"excerpt":{"rendered":"
Two decades of microprocessor architecture driven by quantitative 90\/10 optimization has delivered an extraordinary 1000-fold improvement in microprocessor performance, enabled by transistor scaling which improved density, speed, and energy. Recent generations of technology have produced limited benefits in transistor speed and power, so as a result the industry has turned to multicore parallelism for performance […]<\/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":[11,3],"tags":[468,1782,452,298],"class_list":["post-5950","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-clustering","tag-computer-science","tag-heterogeneous-systems","tag-optimization"],"views":4908,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5950","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=5950"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5950\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5950"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5950"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5950"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}