The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Accelerator: using data parallelism to program GPUs for general-purpose uses
Abstract
GPUs are difficult to program for general-purpose uses. Programmers can either learn graphics APIs and convert their applications to use graphics pipeline operations or they can use stream programming abstractions of GPUs. We describe Accelerator, a system that uses data parallelism to program GPUs for general-purpose uses instead. Programmers use a conventional imperative programming language and a library that provides only high-level data-parallel operations. No aspects of GPUs are exposed to programmers. The library implementation compiles the data-parallel operations on the fly to optimized GPU pixel shader code and API calls.We describe the compilation techniques used to do this. We evaluate the effectiveness of using data parallelism to program GPUs by providing results for a set of compute-intensive benchmarks. We compare the performance of Accelerator versions of the benchmarks against hand-written pixel shaders. The speeds of the Accelerator versions are typically within 50% of the speeds of hand-written pixel shader code. Some benchmarks significantly outperform C versions on a CPU: they are up to 18 times faster than C code running on a CPU.
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Owens, J. D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A. E., & Purcell, T. J. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01012.x · Cited by 2,101
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation
Klöckner, A., Pinto, N., Lee, Y., Catanzaro, B., Ivanov, P., & Fasih, A. (2012) · Parallel Computing · doi:10.1016/j.parco.2011.09.001 · Cited by 573
Program optimization space pruning for a multithreaded gpu
Ryoo, S., Rodrigues, C. I., Stone, S. S., Baghsorkhi, S. S., Ueng, S.-Z., Stratton, J. A., & Hwu, W.-M. W. (2008) · Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization · doi:10.1145/1356058.1356084 · Cited by 285
Accelerating advanced MRI reconstructions on GPUs
Stone, S. S., Haldar, J. P., Tsao, S. C., Hwu, W.-M. W., Sutton, B. P., & Liang, Z.-P. (2008) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2008.05.013 · Cited by 249
Program optimization carving for GPU computing
Ryoo, S., Rodrigues, C. I., Stone, S. S., Stratton, J. A., Ueng, S.-Z., Baghsorkhi, S. S., & Hwu, W.-M. W. (2008) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2008.05.011 · Cited by 126
Metadata
- Authors
- Tarditi, D., Puri, S., & Oglesby, J.
- Venue
- Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
- Publisher
- ACM
- Published
- 2006-10-20
- Pages
- 325-335
- OpenAlex
- W2163229756
- Cited by
- 294
- References
- 15
- FWCI
- 24.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tarditi, D., Puri, S., & Oglesby, J. (2006). Accelerator: using data parallelism to program GPUs for general-purpose uses. Proceedings of the 12th international conference on Architectural support for programming languages and operating systems. https://doi.org/10.1145/1168857.1168898
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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