A programming model which allows users to program with high productivity and which produces high performance executions has been a goal for decades. This dissertation makes progress towards this elusive goal by describing the design and implementation of the Galois system, a parallel programming model for shared-memory, multicore machines. Central to the design is the idea that scheduling of a program can be decoupled from the core computational operator and data structures. However, efficient programs often require application-specific scheduling to achieve best performance. To bridge this gap, an extensible and abstract scheduling policy language is proposed, which allows programmers to focus on selecting high-level scheduling policies while delegating the tedious task of implementing the policy to a scheduler synthesizer and runtime system. Implementations of deterministic and prioritized scheduling also are described. An evaluation of a well-studied benchmark suite reveals that factoring programs into operators, schedulers and data structures can produce significant performance improvements over unfactored approaches. Comparison of the Galois system with existing programming models for graph analytics shows significant performance improvements, often orders of magnitude more, due to (1) better support for the restrictive programming models of existing systems and (2) better support for more sophisticated algorithms and scheduling, which cannot be expressed in other systems. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/30536 |
| Date | 04 September 2015 |
| Creators | Nguyen, Donald Do |
| Contributors | Pingali, Keshav |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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