Molecular Dynamics (MD) simulation is a computationally intensive application used in multiple fields. It can exploit a distributed environment due to inherent computational parallelism. However, most of the existing implementations focus on performance enhancement. They may not provide fault-tolerance for every time-step. MapReduce is a framework first proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and fault- tolerance for node failure during run-time make it very popular not only for commercial applications but also in scientific computing. In this thesis, we develop a novel communication-free and each time-step fault- tolerant solution for MD simulation based on Hadoop MapReduce (MDMR). Through emulation of Hadoop MapReduce and introduction of a run-time program monitor, we can predict the execution time of a given size MD simulation system. We also demonstrate the performance and energy consumption improvement from implementing MDMR in a hybrid MapReduce environment with GPU hardware (MDMR-G). To evaluate MDMR, we construct a 32 node MapReduce cluster and a run-time MapReduce program monitor. We emulate MDMR and propose a prediction formula of MDMR execution time for Map and Reduce stages. The emulation results demonstrate our formula can predict MDMR execution time within 9.1% variance. Our run-time monitor shows that MDMR can obtain high computational power efficiency for large MD simulation systems. We also build a hybrid MapReduce cluster with GPGPU. MDMR in this environment obtains 20 times speedup and reduces energy consumption 95% compared with the same size cluster without GPGPU accelerators.<\/p>\n","protected":false},"excerpt":{"rendered":"
Molecular Dynamics (MD) simulation is a computationally intensive application used in multiple fields. It can exploit a distributed environment due to inherent computational parallelism. However, most of the existing implementations focus on performance enhancement. They may not provide fault-tolerance for every time-step. MapReduce is a framework first proposed by Google for processing huge amounts of […]<\/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":[66,89,3,12],"tags":[1790,589,14,261,112,20,997,1783,390],"class_list":["post-6418","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-nvidia-cuda","category-paper","category-physics","tag-chemistry","tag-cluster-computing","tag-cuda","tag-mapreduce","tag-molecular-dynamics","tag-nvidia","tag-nvidia-geforce-9400-gt","tag-physics","tag-thesis"],"views":2562,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6418","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=6418"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6418\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6418"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6418"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}