{"id":163731,"date":"2012-10-09T00:00:00","date_gmt":"2012-10-09T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/composable-incremental-and-iterative-data-parallel-computation-with-naiad\/"},"modified":"2018-10-16T20:19:55","modified_gmt":"2018-10-17T03:19:55","slug":"composable-incremental-and-iterative-data-parallel-computation-with-naiad","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/composable-incremental-and-iterative-data-parallel-computation-with-naiad\/","title":{"rendered":"Composable Incremental and Iterative Data-Parallel Computation with Naiad"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We report on the design and implementation of Naiad, a set of declarative data-parallel language extensions and an associated runtime supporting efficient and composable incremental and iterative computation.  This combination is enabled by a new computational model we call differential dataflow, in which incremental computation can be performed using a partial, rather than total, order on time.<\/p>\n<p>Naiad extends standard batch data-parallel processing models like MapReduce, Hadoop, and Dryad\/DryadLINQ, to support efficient incremental updates to the inputs in the manner of a stream processing system, while at the same time enabling arbitrarily nested fixed-point iteration.  In this paper, we evaluate a prototype of Naiad that uses shared memory on a single multi-core computer.  We apply Naiad to various computations, including several graph algorithms, and observe good scaling properties and efficient incremental recomputation.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We report on the design and implementation of Naiad, a set of declarative data-parallel language extensions and an associated runtime supporting efficient and composable incremental and iterative computation. This combination is enabled by a new computational model we call differential dataflow, in which incremental computation can be performed using a partial, rather than total, order [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"mcsherry"},{"type":"user_nicename","value":"risaacs"},{"type":"user_nicename","value":"misard"},{"type":"user_nicename","value":"derekmur"}],"msr_publishername":"Microsoft 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13:23:55","post_modified":"2017-06-01 13:16:10","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/naiad\/","post_excerpt":"Download: Naiad is now available under the Apache 2.0 open-source license from Github (source) and NuGet.org (binary packages). For more details about the software release, see the online Naiad documentation. The Naiad project is an investigation of data-parallel dataflow computation, like Dryad and DryadLINQ, but with a focus on low-latency streaming and cyclic computations. Naiad introduces a new computational model, timely dataflow, which combines low-latency asynchronous message flow with lightweight coordination when required. 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