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Archit. Code Optim."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            We propose IR2V\n            <jats:sc>EC<\/jats:sc>\n            , a Concise and Scalable encoding infrastructure to represent programs as a distributed embedding in continuous space. This distributed embedding is obtained by combining representation learning methods with flow information to capture the syntax as well as the semantics of the input programs. As our infrastructure is based on the Intermediate Representation (IR) of the source code, obtained embeddings are both language and machine independent. The entities of the IR are modeled as relationships, and their representations are learned to form a\n            <jats:italic>seed embedding vocabulary<\/jats:italic>\n            . Using this infrastructure, we propose two incremental encodings:\n            <jats:italic>Symbolic<\/jats:italic>\n            and\n            <jats:italic>Flow-Aware<\/jats:italic>\n            .\n            <jats:italic>Symbolic<\/jats:italic>\n            encodings are obtained from the\n            <jats:italic>seed embedding vocabulary<\/jats:italic>\n            , and\n            <jats:italic>Flow-Aware<\/jats:italic>\n            encodings are obtained by augmenting the\n            <jats:italic>Symbolic<\/jats:italic>\n            encodings with the flow information.\n          <\/jats:p>\n          <jats:p>\n            We show the effectiveness of our methodology on two optimization tasks (Heterogeneous device mapping and Thread coarsening). Our way of representing the programs enables us to use non-sequential models resulting in orders of magnitude of faster training time. Both the encodings generated by IR2V\n            <jats:sc>EC<\/jats:sc>\n            outperform the existing methods in both the tasks, even while using\n            <jats:italic>simple<\/jats:italic>\n            machine learning models. In particular, our results improve or match the state-of-the-art speedup in 11\/14 benchmark-suites in the device mapping task across two platforms and 53\/68 benchmarks in the thread coarsening task across four different platforms. When compared to the other methods, our embeddings are\n            <jats:italic>more scalable<\/jats:italic>\n            ,\n            <jats:italic>is non-data-hungry<\/jats:italic>\n            , and\n            <jats:italic>has better Out-Of-Vocabulary (OOV) characteristics<\/jats:italic>\n            .\n          <\/jats:p>","DOI":"10.1145\/3418463","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T19:29:25Z","timestamp":1608665365000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":69,"title":["IR2V\n            <scp>EC<\/scp>"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1393-7321","authenticated-orcid":false,"given":"S.","family":"VenkataKeerthy","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India"}]},{"given":"Rohit","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India"}]},{"given":"Shalini","family":"Jain","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India"}]},{"given":"Maunendra Sankar","family":"Desarkar","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India"}]},{"given":"Ramakrishna","family":"Upadrasta","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India"}]},{"given":"Y. 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