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The Tao of Parallelism in Algorithms (2011)
| Content Provider | CiteSeerX |
|---|---|
| Author | Pingali, Keshav Nguyen, Donald Kulkarni, Milind Burtscher, Martin Hassaan, M. Amber Kaleem, Rashid Lee, Tsung-Hsien Lenharth, Andrew Manevich, Roman Méndez-Lojo, Mario Prountzos, Dimitrios Sui, Xin |
| Description | 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 “regular ” 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 “irregular ” data structures like graphs, trees, and sets. To address the need for better abstractions, we introduce a datacentric 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 In PLDI |
| File Format | |
| Language | English |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Algorithmic Property Parallel Programming Community Structural Analysis Suitable Abstraction New Application Area Key Data Structure Irregular Data Structure Machine Learning Data Structure Datacentric Formulation Regular Algorithm Main Abstraction Dense Array Operator Formulation Network Analysis Dependence Graph |
| Content Type | Text |
| Resource Type | Article |
