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In this paper, we attempt to marry, for the first time, the best relevant techniques in parallel computing, where kernel summations are in low dimensions, with the best general\u2010dimension algorithms from the machine learning literature. We provide the first distributed implementation of kernel summation framework that can utilize: (i) various types of deterministic and probabilistic approximations that may be suitable for low and high\u2010dimensional problems with a large number of data points; (ii) any multidimensional binary tree using both distributed memory and shared memory parallelism; and (iii) a dynamic load balancing scheme to adjust work imbalances during the computation. Our hybrid message passing interface (MPI)\/OpenMP codebase has wide applicability in providing a general framework to accelerate the computation of many popular machine learning methods. Our experiments show scalability results for kernel density estimation on a synthetic ten\u2010dimensional dataset containing over one billion points and a subset of the Sloan Digital Sky Survey Data up to 6144 cores. \u00a9 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013<\/jats:p>","DOI":"10.1002\/sam.11207","type":"journal-article","created":{"date-parts":[[2013,10,24]],"date-time":"2013-10-24T22:44:21Z","timestamp":1382654661000},"page":"1-13","source":"Crossref","is-referenced-by-count":7,"title":["A distributed kernel summation framework for general\u2010dimension machine learning"],"prefix":"10.1002","volume":"7","author":[{"given":"Dongryeol","family":"Lee","sequence":"first","affiliation":[]},{"given":"Piyush","family":"Sao","sequence":"additional","affiliation":[]},{"given":"Richard","family":"Vuduc","sequence":"additional","affiliation":[]},{"given":"Alexander G.","family":"Gray","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2013,10,24]]},"reference":[{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177704472"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1137\/1109020"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"e_1_2_9_6_2","first-page":"2","volume-title":"Optimization, and Beyond","author":"Scholkopf B.","year":"2002"},{"key":"e_1_2_9_7_2","first-page":"747","volume-title":"Advances in Neural Information Processing Systems","author":"Lee D.","year":"2006"},{"key":"e_1_2_9_8_2","unstructured":"D.LeeandA.Gray Faster Gaussian summation: theory and experiment. 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