In this thesis, we describe a new classification of auto-tuning methodologies spanning from low-level optimizations to high-level algorithmic tuning. This classification spectrum of auto-tuning methods encompasses the space of tuning parameters from low-level optimizations (such as block sizes, iteration ordering, vectorization, etc.) to high-level algorithmic choices (such as whether to use an iterative solver or a direct solver). We present and analyze four novel auto-tuning systems that incorporate several techniques that fall along a spectrum from the low-level to the high-level: i) a multiplatform, auto-tuning parallel code generation framework for generalized stencil loops, ii) an auto-tunable algorithm for solving dense triangular systems, iii) an auto-tunable multigrid solver for sparse linear systems, and iv) tuned statistical regression techniques for fine-tuning wind forecasts and resource estimations to assist in the integration of wind resources into the electrical grid. We also include a project assessment report for a wind turbine installation for the City of Cambridge to highlight an area of application (wind prediction and resource assessment) where these computational auto-tuning techniques could prove useful in the future.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this thesis, we describe a new classification of auto-tuning methodologies spanning from low-level optimizations to high-level algorithmic tuning. This classification spectrum of auto-tuning methods encompasses the space of tuning parameters from low-level optimizations (such as block sizes, iteration ordering, vectorization, etc.) to high-level algorithmic choices (such as whether to use an iterative solver or […]<\/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":[36,11,3],"tags":[1787,215,1782,20,234,298,390],"class_list":["post-8548","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-code-generation","tag-computer-science","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-optimization","tag-thesis"],"views":2585,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8548","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=8548"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8548\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}