Subgraph isomorphism is a fundamental graph problem with applications in diverse domains from biology to social network analysis. Of particular interest is molecular matching, which uses a subgraph isomorphism formulation for the drug discovery process. While subgraph isomorphism is known to be NP-complete and computationally expensive, in the molecular matching formulation a number of domain constraints allow for efficient implementations. This paper presents SIGMo, a high-throughput, portable subgraph isomorphism framework for GPUs, specifically designed for batch molecular matching. SIGMo takes advantage of the specific domain formulation to provide a more efficient filter-and-join strategy: the framework introduces a novel multi-level iterative filtering technique based on neighborhood signature encoding to efficiently prune candidates prior to a GPU-optimized join phase using a stackbased DFS traversal. The GPU implementation is written in SYCL, allowing portable execution on AMD, Intel, and NVIDIA GPUs. Our experimental evaluation on a large dataset from ZINC demonstrates up to 1470\u00d7 speedup over state-of-the-art subgraph isomorphism frameworks, and achieves a throughput of 7.7 billion matches per second on a cluster with 256 GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"
Subgraph isomorphism is a fundamental graph problem with applications in diverse domains from biology to social network analysis. Of particular interest is molecular matching, which uses a subgraph isomorphism formulation for the drug discovery process. While subgraph isomorphism is known to be NP-complete and computationally expensive, in the molecular matching formulation a number of domain […]<\/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":[10,66,11,89,3],"tags":[1781,1790,1782,14,242,20,2115,2118,176],"class_list":["post-30085","post","type-post","status-publish","format-standard","hentry","category-biology","category-chemistry","category-computer-science","category-nvidia-cuda","category-paper","tag-biology","tag-chemistry","tag-computer-science","tag-cuda","tag-mpi","tag-nvidia","tag-nvidia-v100","tag-oneapi","tag-package"],"views":4654,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/30085","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=30085"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/30085\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=30085"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=30085"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=30085"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}