The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server
Open accessAbstract
Large-scale deep learning requires huge computational resources to train a multi-layer neural network. Recent systems propose using 100s to 1000s of machines to train networks with tens of layers and billions of connections. While the computation involved can be done more efficiently on GPUs than on more traditional CPU cores, training such networks on a single GPU is too slow and training on distributed GPUs can be inefficient, due to data movement overheads, GPU stalls, and limited GPU memory. This paper describes a new parameter server, called GeePS, that supports scalable deep learning across GPUs distributed among multiple machines, overcoming these obstacles. We show that GeePS enables a state-of-the-art single-node GPU implementation to scale well, such as to 13 times the number of training images processed per second on 16 machines (relative to the original optimized single-node code). Moreover, GeePS achieves a higher training throughput with just four GPU machines than that a state-of-the-art CPU-only system achieves with 108 machines.
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Cites
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PipeDream: generalized pipeline parallelism for DNN training
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Self-citationDeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
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Metadata
- Authors
- Cui, H., Zhang, H., Ganger, G. R., Gibbons, P. B., & Xing, E. P.
- Venue
- Proceedings of the Eleventh European Conference on Computer Systems
- Publisher
- ACM
- Published
- 2016-04-18
- Pages
- 1-16
- OpenAlex
- W2339765813
- Cited by
- 286
- References
- 32
- FWCI
- 31.73
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Cui, H., Zhang, H., Ganger, G. R., Gibbons, P. B., & Xing, E. P. (2016). GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server. Proceedings of the Eleventh European Conference on Computer Systems. https://doi.org/10.1145/2901318.2901323
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