Spatial hardware implementation for sparse graph algorithms in GraphStep
How do we develop programs that are easy to express, easy to reason about, and able to achieve high performance on massively parallel machines? To address this problem, we introduce GraphStep, a domain-specific compute model that captures algorithms that act on static, irregular, sparse graphs. In G...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81195 http://hdl.handle.net/10220/39184 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | How do we develop programs that are easy to express, easy to reason about, and able to achieve high performance on massively parallel machines? To address this problem, we introduce GraphStep, a domain-specific compute model that captures algorithms that act on static, irregular, sparse graphs. In GraphStep, algorithms are expressed directly without requiring the programmer to explicitly manage parallel synchronization, operation ordering, placement, or scheduling details. Problems in the sparse graph domain are usually highly concurrent and communicate along graph edges. Exposing concurrency and communication structure allows scheduling of parallel operations and management of communication that is necessary for performance on a spatial computer. We study the performance of a semantic network application, a shortest-path application, and a max-flow/min-cut application. We introduce a language syntax for GraphStep applications. The total speedup over sequential versions of the applications studied ranges from a factor of 19 to a factor of 15,000. Spatially-aware graph optimizations (e.g., node decomposition, placement and route scheduling) delivered speedups from 3 to 30 times over a spatially-oblivious mapping. |
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