The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An integrated GPU power and performance model
Abstract
GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Performance optimization for multi-core processors has been a challenge for programmers. Furthermore, optimizing for power consumption is even more difficult. Unfortunately, as a result of the high number of processors, the power consumption of many-core processors such as GPUs has increased significantly.
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Williams, S., Waterman, A., & Patterson, D. A. (2009) · Communications of the ACM · doi:10.1145/1498765.1498785 · Cited by 2,798
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Embodied large language models enable robots to complete complex tasks in unpredictable environments
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Training a Probabilistic Graphical Model With Resistive Switching Electronic Synapses
Eryilmaz, S. B., Neftci, E., Joshi, S., Kim, S., BrightSky, M., Lung, H.-L., Lam, C., Cauwenberghs, G., & Wong, H.-S. P. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2616483 · Cited by 34
iMLBench: A Machine Learning Benchmark Suite for CPU-GPU Integrated Architectures
Zhang, C., Zhang, F., Guo, X., He, B., Zhang, X., & Du, X. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3046870 · Cited by 27
Metadata
- Authors
- Hong, S., & Kim, H.
- Venue
- Proceedings of the 37th annual international symposium on Computer architecture
- Publisher
- ACM
- Published
- 2010-06-19
- Pages
- 280-289
- ISSN
- 1063-6897 (ISSN-L)
- OpenAlex
- W2033597569
- Cited by
- 483
- References
- 1
- FWCI
- 27.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hong, S., & Kim, H. (2010). An integrated GPU power and performance model. Proceedings of the 37th annual international symposium on Computer architecture. https://doi.org/10.1145/1815961.1815998
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
