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High Performance Stencil Code Generation with Lift
| Content Provider | Semantic Scholar |
|---|---|
| Author | Hagedorn, Bastian Stoltzfus, Larisa |
| Copyright Year | 2017 |
| Abstract | Stencil computations are widely used from physical simulations to machine-learning. They are embarrassingly parallel and perfectly fit modern hardware such as Graphic Processing Units. Although stencil computations have been extensively studied, optimizing them for increasingly diverse hardware remains challenging. Domain Specific Languages (DSLs) have raised the programming abstraction and offer good performance. However, this places the burden on DSL implementers who have to write almost full-fledged parallelizing compilers and optimizers. Lift has recently emerged as a promising approach to achieve performance portability and is based on a small set of reusable parallel primitives that DSL or library writers can build upon. Lift’s key novelty is in its encoding of optimizations as a system of extensible rewrite rules which are used to explore the optimization space. However, Lift has mostly focused on linear algebra operations and it remains to be seen whether this approach is applicable for other domains. This paper demonstrates how complex multidimensional stencil code and optimizations such as tiling are expressible using compositions of simple 1D Lift primitives. By leveraging existing Lift primitives and optimizations, we only require the addition of two primitives and one rewrite rule to do so. Our results show that this approach outperforms existing compiler approaches and hand-tuned codes. CCS Concepts • Software and its engineering → Parallel programming languages; Compilers; Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. CGO’18, February 24–28, 2018, Vienna, Austria © 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery. ACM ISBN 978-1-4503-5617-6/18/02. . . $15.00 https://doi.org/10.1145/3168824 |
| File Format | PDF HTM / HTML |
| DOI | 10.1145/3179541.3168824 |
| Alternate Webpage(s) | https://www.research.ed.ac.uk/portal/files/53017640/cgo18main_p93_p_f0710a0_34716_submitted.pdf |
| Alternate Webpage(s) | https://doi.org/10.1145/3179541.3168824 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
