000165111 001__ 165111
000165111 005__ 20190316235101.0
000165111 037__ $$aCONF
000165111 245__ $$aParallelizing Machine Learning- Functionally: A Framework and Abstractions for Parallel Graph Processing
000165111 269__ $$a2011
000165111 260__ $$c2011
000165111 336__ $$aConference Papers
000165111 520__ $$aImplementing machine learning algorithms for large data, such as the Web graph and social networks, is challenging. Even though much research has focused on making sequential algorithms more scalable, their running times continue to be prohibitively long. Meanwhile, parallelization remains a formidable challenge for this class of problems, despite frameworks like MapReduce which hide much of the associated complexity. We present a framework for implementing parallel and distributed machine learning algorithms on large graphs, flexibly, through the use of functional programming abstractions. Our aim is a system that allows researchers and practitioners to quickly and easily implement (and experiment with) their algorithms in a parallel or distributed setting. We introduce functional combinators for the flexible composition of parallel, aggregation, and sequential steps. To the best of our knowledge, our system is the first to avoid inversion of control in a (bulk) synchronous parallel model.
000165111 6531_ $$aParallel programming
000165111 6531_ $$adistributed programming
000165111 6531_ $$amachine learning
000165111 6531_ $$agraph processing
000165111 700__ $$0240993$$aHaller, Philipp$$g172057
000165111 700__ $$0242185$$aMiller, Heather$$g191683
000165111 7112_ $$a2nd Annual Scala Workshop$$cStanford, California, USA$$dJune 2, 2011
000165111 8564_ $$s174738$$uhttps://infoscience.epfl.ch/record/165111/files/scalawksp11.pdf$$yPostprint$$zPostprint
000165111 909C0 $$0252187$$pLAMP$$xU10409
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000165111 937__ $$aEPFL-CONF-165111
000165111 973__ $$aEPFL$$rREVIEWED$$sACCEPTED
000165111 980__ $$aCONF