The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
Open accessAbstract
Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values. We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.
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Puschel, M., Moura, J. M. F., Johnson, J. R., Padua, D., Veloso, M. M., Singer, B. W., Xiong, J., Franchetti, F., Gacic, A., Voronenko, Y., et al. (2005) · Proceedings of the IEEE · doi:10.1109/jproc.2004.840306 · Cited by 826
Memory---Sequoia: programming the memory hierarchy
Fatahalian, K., Dally, W. J., Hanrahan, P., Horn, D. R., Knight, T. J., Leem, L., Houston, M., Park, J. Y., Erez, M., Ren, M., et al. (2006) · Proceedings of the 2006 ACM/IEEE conference on Supercomputing - SC '06 · doi:10.1145/1188455.1188543 · Cited by 452
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Chen, J., Paris, S., & Durand, F. (2007) · ACM Transactions on Graphics · doi:10.1145/1276377.1276506 · Cited by 395
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Self-citationPetaBricks: a language and compiler for algorithmic choice
Ansel, J., Chan, C., Wong, Y. L., Olszewski, M., Zhao, Q., Edelman, A., & Amarasinghe, S. (2009) · Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/1542476.1542481 · Cited by 355
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A stream compiler for communication-exposed architectures
Gordon, M. I., Thies, W., Karczmarek, M., Lin, J., Meli, A. S., Lamb, A. A., Leger, C., Wong, J., Hoffmann, H., Maze, D., et al. (2002) · Proceedings of the 10th international conference on Architectural support for programming languages and operating systems · doi:10.1145/605397.605428 · Cited by 337
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Nguyen, A., Satish, N., Chhugani, J., Kim, C., & Dubey, P. (2010) · 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis · doi:10.1109/sc.2010.2 · Cited by 290
Brook for GPUs: stream computing on graphics hardware
Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., & Hanrahan, P. (2004) · ACM SIGGRAPH 2004 Papers · doi:10.1145/1186562.1015800 · Cited by 254
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Metadata
- Authors
- Ragan-Kelley, J., Barnes, C., Adams, A., Paris, S., Durand, F., & Amarasinghe, S.
- Venue
- ACM SIGPLAN Notices
- Published
- 2013-06-16
- Volume
- 48
- Issue
- 6
- Pages
- 519-530
- ISSN
- 0362-1340 (ISSN-L)
- 1558-1160
- OpenAlex
- W4251637954
- Cited by
- 513
- References
- 20
- FWCI
- 17.42
- Type
- journal article
- Language
- en
- Open access
- green
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