With the growing complexity of software, the number of test cases needed for effective validation is extremely large. Executing these large test suites is expensive and time consuming, putting an enormous pressure on the software development cycle. In previous work, we proposed using Graphics Processing Units (GPUs) to accelerate test execution by running test cases in parallel on the GPU threads. However, the complexity of GPU programming poses challenges to the usability and effectiveness of the proposed approach. In this paper we present ParTeCL – a compiler-assisted framework to automatically generate GPU code from sequential programs and execute their tests in parallel on the GPU. We show feasibilitiy and performance achieved when executing test suites for 9 programs from an industry standard benchmark suite on the GPU. ParTeCL achieves an average speedup of 16x when compared to a single CPU for these benchmarks.<\/p>\n","protected":false},"excerpt":{"rendered":"
With the growing complexity of software, the number of test cases needed for effective validation is extremely large. Executing these large test suites is expensive and time consuming, putting an enormous pressure on the software development cycle. In previous work, we proposed using Graphics Processing Units (GPUs) to accelerate test execution by running test cases […]<\/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":[11,90,3],"tags":[451,955,1782,1793,176],"class_list":["post-17408","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-benchmarking","tag-compilers","tag-computer-science","tag-opencl","tag-package"],"views":2227,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17408","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=17408"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17408\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17408"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17408"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}