{"id":162373,"date":"2012-04-01T00:00:00","date_gmt":"2012-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/madlinq-large-scale-distributed-matrix-computation-for-the-cloud\/"},"modified":"2018-10-16T20:16:53","modified_gmt":"2018-10-17T03:16:53","slug":"madlinq-large-scale-distributed-matrix-computation-for-the-cloud","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/madlinq-large-scale-distributed-matrix-computation-for-the-cloud\/","title":{"rendered":"MadLINQ: Large-Scale Distributed Matrix Computation for the Cloud"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The computation core of many data-intensive applications can be best expressed as matrix computations. The MadLINQ project addresses the following two important research problems: the need for a highly scalable, efficient and fault-tolerant matrix computation system that is also easy to program, and the seamless integration of such specialized execution engines in a general purpose data-parallel computing system.<\/p>\n<p>MadLINQ exposes a unified programming model to both matrix algorithm and application developers. Matrix algorithms are expressed as sequential programs operating on tiles (i.e., sub-matrices). For application developers, MadLINQ provides a distributed matrix computation library for .NET languages. Via the LINQ technology, MadLINQ also seamlessly integrates with DryadLINQ, a data-parallel computing system focusing on relational algebra.<\/p>\n<p>The system automatically handles the parallelization and distributed execution of programs on a large cluster. It outperforms current state-of-the-art systems by employing two key techniques, both of which are enabled by the matrix abstraction: exploiting extra parallelism using fine-grained pipelining and efficient on-demand failure recovery using a distributed fault-tolerant execution engine. We describe the design and implementation of MadLINQ and evaluate system performance using several real-world applications.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The computation core of many data-intensive applications can be best expressed as matrix computations. The MadLINQ project addresses the following two important research problems: the need for a highly scalable, efficient and fault-tolerant matrix computation system that is also easy to program, and the seamless integration of such specialized execution engines in a general purpose [&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":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"EuroSys 2012: 7th ACM European Conference on Computer Systems, Berne, Switzerland","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"EuroSys 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