{"id":29162,"date":"2024-03-24T17:32:26","date_gmt":"2024-03-24T15:32:26","guid":{"rendered":"https:\/\/hgpu.org\/?p=29162"},"modified":"2024-03-24T17:32:26","modified_gmt":"2024-03-24T15:32:26","slug":"parallel-gaussian-process-with-kernel-approximation-in-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=29162","title":{"rendered":"Parallel Gaussian process with kernel approximation in CUDA"},"content":{"rendered":"<p>This paper introduces a parallel implementation in CUDA\/C++ of the Gaussian process with a decomposed kernel. This recent formulation, introduced by Joukov and Kuli\u0107 (2022), is characterized by an approximated &#8212; but much smaller &#8212; matrix to be inverted compared to plain Gaussian process. However, it exhibits a limitation when dealing with higher-dimensional samples which degrades execution times. The solution presented in this paper relies on parallelizing the computation of the predictive posterior statistics on a GPU using CUDA and its libraries. The CPU code and GPU code are then benchmarked on different CPU-GPU configurations to show the benefits of the parallel implementation on GPU over the CPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper introduces a parallel implementation in CUDA\/C++ of the Gaussian process with a decomposed kernel. This recent formulation, introduced by Joukov and Kuli\u0107 (2022), is characterized by an approximated &#8212; but much smaller &#8212; matrix to be inverted compared to plain Gaussian process. However, it exhibits a limitation when dealing with higher-dimensional samples which [&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":[451,1782,14,37,20,2008,2046,176],"class_list":["post-29162","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-benchmarking","tag-computer-science","tag-cuda","tag-linear-algebra","tag-nvidia","tag-nvidia-geforce-gtx-1050","tag-nvidia-geforce-rtx-2080","tag-package"],"views":1200,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29162","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=29162"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29162\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29162"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29162"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}