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| Content Provider | ACM Digital Library |
|---|---|
| Author | Oancea, Cosmin E. Elsman, Martin Henriksen, Troels |
| Abstract | We present a shape inference analysis for a purely-functional language, named Futhark, that supports nested parallelism via array combinators such as map, reduce, filter}, and scan}. Our approach is to infer code for computing precise shape information at run-time, which in the most common cases can be effectively optimized by standard compiler optimizations. Instead of restricting the language or sacrificing ease of use, the language allows the occasional shape-dynamic, and even shape-misbehaving, constructs. Inherently shape-dynamic code is treated with a fall-back technique that preserves, asymptotically, the number of operations of the program and that computes and returns the array's shape alongside with its value. This approach leads to a shape-dependent system with existentially-quantified types, where static shape inference corresponds to eliminating existential quantifications from the types of program expressions. We optimize the common case to negligible overhead via size slicing: a technique that separates the computation of the array's shape from its values. This allows the shape to be calculated in advance and to be used to instantiate the previously existentially-quantified shapes of the value slice. We report negligible overhead, on several mini-benchmarks and three real-world applications. |
| Starting Page | 31 |
| Ending Page | 42 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450330404 |
| DOI | 10.1145/2636228.2636238 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-09-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Functional language Size analysis Dependent types |
| Content Type | Text |
| Resource Type | Article |
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