{"id":258051,"date":"2011-06-04T20:42:14","date_gmt":"2011-06-05T03:42:14","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=258051"},"modified":"2018-10-16T20:21:43","modified_gmt":"2018-10-17T03:21:43","slug":"the-tao-of-parallelism-in-algorithms","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-tao-of-parallelism-in-algorithms\/","title":{"rendered":"The tao of parallelism in algorithms"},"content":{"rendered":"<p>For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in &#8220;regular&#8221; algorithms that use dense arrays, such as finite-differences and FFTs. In this paper, we argue that the dependence graph is not a suitable abstraction for algorithms in new application areas like machine learning and network analysis in which the key data structures are &#8220;irregular&#8221; data structures like graphs, trees, and sets.<\/p>\n<p>To address the need for better abstractions, we introduce a data-centric formulation of algorithms called the operator formulation in which an algorithm is expressed in terms of its action on data structures. This formulation is the basis for a structural analysis of algorithms that we call tao-analysis. Tao-analysis can be viewed as an abstraction of algorithms that distills out algorithmic properties important for parallelization. It reveals that a generalized form of data-parallelism called amorphous data-parallelism is ubiquitous in algorithms, and that, depending on the tao-structure of the algorithm, this parallelism may be exploited by compile-time, inspector-executor or optimistic parallelization, thereby unifying these seemingly unrelated parallelization techniques. Regular algorithms emerge as a special case of irregular algorithms, and many application-specific optimization techniques can be generalized to a broader context.<\/p>\n<p>These results suggest that the operator formulation and tao-analysis of algorithms can be the foundation of a systematic approach to parallel programming.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in &#8220;regular&#8221; algorithms that use dense arrays, such as finite-differences and FFTs. 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