The DOI matches its Crossref record.
Twin peaks: a software platform for heterogeneous computing on general-purpose and graphics processors
Abstract
Modern processors are evolving into hybrid, heterogeneous processors with both CPU and GPU cores used for general purpose computation. Several languages such as Brook, CUDA, and more recently OpenCL are being developed to fully harness the potential of these processors. These languages typically involve the control code running on the CPU and the performance-critical, data-parallel kernel code running on the GPUs.
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Claims the paper makes
Claims other papers make about this one
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Several initial works report GPUs provide up to 100× to 1000× speedup; other researchers claim that on applying careful optimizations on both CPUs and GPUs, CPUs may equal or even outperform GPUs.
“Further, while several initial works report that GPUs provide up to 100× to 1000× speedup, other researchers claim that on applying careful optimizations on both CPUs and GPUs; CPUs may equal or even outperform the performance of GPUs [Gummaraju et al. 2010;Lee et al. 2010].”
From A Survey of CPU-GPU Heterogeneous Computing Techniques (2015) · cites this paper as Gummaraju et al. 2010
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No references could be resolved yet.
Research works printed but not linked: 21
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Cited by
Sorted by citations
A Survey of CPU-GPU Heterogeneous Computing Techniques
Mittal, S., & Vetter, J. S. (2015) · ACM Computing Surveys · doi:10.1145/2788396 · Cited by 568
Metadata
- Authors
- Gummaraju, J., Morichetti, L., Houston, M., Sander, B., Gaster, B. R., & Zheng, B.
- Affiliations
- Advanced Micro Devices, Sunnyvale, CA, USA
- Venue
- Proceedings of the 19th international conference on Parallel architectures and compilation techniques
- Publisher
- ACM
- Published
- 2010-09-11
- Pages
- 205-216
- Cited by
- 59
- References
- 0
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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