{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T22:51:06Z","timestamp":1767912666971,"version":"3.49.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p>\n            This paper demonstrates G\n            <jats:sup>3<\/jats:sup>\n            , a framework for &lt;u&gt;G&lt;\/u&gt;raph Neural Network (GNN) training, tailored from &lt;u&gt;G&lt;\/u&gt;raph processing systems on &lt;u&gt;G&lt;\/u&gt;raphics processing units (GPUs). G\n            <jats:sup>3<\/jats:sup>\n            aims at improving the efficiency of GNN training by supporting graph-structured operations using parallel graph processing systems. G\n            <jats:sup>3<\/jats:sup>\n            enables users to leverage the massive parallelism and other architectural features of GPUs in the following two ways: building GNN layers by writing sequential C\/C++ code with a set of flexible APIs (Application Programming Interfaces); creating GNN models with essential GNN operations and layers provided in G\n            <jats:sup>3<\/jats:sup>\n            . The runtime system of G\n            <jats:sup>3<\/jats:sup>\n            automatically executes the user-defined GNNs on the GPU, with a series of graph-centric optimizations enabled. We demonstrate the steps of developing some popular GNN models with G\n            <jats:sup>3<\/jats:sup>\n            , and the superior performance of G\n            <jats:sup>3<\/jats:sup>\n            against existing GNN training systems, i.e., PyTorch and TensorFlow.\n          <\/jats:p>","DOI":"10.14778\/3415478.3415482","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T18:46:35Z","timestamp":1600109195000},"page":"2813-2816","source":"Crossref","is-referenced-by-count":34,"title":["G\n            <sup>3<\/sup>"],"prefix":"10.14778","volume":"13","author":[{"given":"Husong","family":"Liu","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengliang","family":"Lu","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Chen","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"265","volume-title":"12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16)","author":"Abadi M.","year":"2016","unstructured":"M. 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