{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T13:38:19Z","timestamp":1761917899797,"version":"3.44.0"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T00:00:00Z","timestamp":1583712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"\u201cImpuls und Vernetzungsfond\u201d of the Helmholtz Association","award":["VH-NG-1241"],"award-info":[{"award-number":["VH-NG-1241"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Parallel Comput."],"published-print":{"date-parts":[[2020,3,31]]},"abstract":"<jats:p>\n            We describe the application of a communication-reduction technique for the PageRank algorithm that dynamically adapts the precision of the data access to the numerical requirements of the algorithm as the iteration converges. Our variable-precision strategy, using a customized precision format based on mantissa segmentation (CPMS), abandons the IEEE 754 single- and double-precision number representation formats employed in the standard implementation of PageRank, and instead handles the data in memory using a customized floating-point format. The customized format enables fast data access in different accuracy, prevents overflow\/underflow by preserving the\n            <jats:sc>IEEE<\/jats:sc>\n            754 double-precision exponent, and efficiently avoids data duplication, since all bits of the original\n            <jats:sc>IEEE<\/jats:sc>\n            754 double-precision mantissa are preserved in memory, but re-organized for efficient reduced precision access. With this approach, the truncated values (omitting significand bits), as well as the original IEEE double-precision values, can be retrieved without duplicating the data in different formats.\n          <\/jats:p>\n          <jats:p>Our numerical experiments on an NVIDIA V100 GPU (Volta architecture) and a server equipped with two Intel Xeon Platinum 8168 CPUs (48 cores in total) expose that, compared with a standard IEEE double-precision implementation, the CPMS-based PageRank completes about 10% faster if high-accuracy output is needed, and about 30% faster if reduced output accuracy is acceptable.<\/jats:p>","DOI":"10.1145\/3380934","type":"journal-article","created":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T08:20:30Z","timestamp":1583742030000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Acceleration of PageRank with Customized Precision Based on Mantissa Segmentation"],"prefix":"10.1145","volume":"7","author":[{"given":"Thomas","family":"Gr\u00fctzmacher","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terry","family":"Cojean","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Goran","family":"Flegar","sequence":"additional","affiliation":[{"name":"Universitat Jaume I, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hartwig","family":"Anzt","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Germany and University of Tennessee, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique S.","family":"Quintana-Ort\u00ed","sequence":"additional","affiliation":[{"name":"Universitat Polit\u00e8cnica de Val\u00e8ncia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,3,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4460"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2834800.2834802"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/1094342019846547"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PARCO.2017.05.006"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1654059.1654078"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/17M1122918"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1137\/17M1140819"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-64203-1_50"},{"key":"e_1_2_1_10_1","volume-title":"Van Loan","author":"Golub Gene H.","year":"1996","unstructured":"Gene H. 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Accuracy and Stability of Numerical Algorithms (2nd ed.). SIAM.","edition":"2"},{"key":"e_1_2_1_16_1","unstructured":"Zhe Jia Marco Maggioni Benjamin Staiger and Daniele P. Scarpazza. 2018. Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking. Technical Report. arxiv:1804.06826 http:\/\/arxiv.org\/abs\/1804.06826."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1137\/130930352"},{"key":"e_1_2_1_18_1","volume-title":"Meyer","author":"Langville Amy N.","year":"2012","unstructured":"Amy N. Langville and Carl D. Meyer. 2012. Google\u2019s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, Princeton, NJ."},{"key":"e_1_2_1_19_1","volume-title":"Whitepaper: NVIDIA Tesla V100 GPU Architecture.","author":"NVIDIA Corp.","year":"2017","unstructured":"NVIDIA Corp. 2017. Whitepaper: NVIDIA Tesla V100 GPU Architecture. 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