CuWide: Towards efficient flow-based training for sparse wide models on GPUs (Extended Abstract)
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Date
2021Type
- Conference Paper
Abstract
In this paper, we propose an efficient GPU-training framework for the large-scale wide models, named cuWide. To fully benefit from the memory hierarchy of GPU, cuWide applies a new flow-based schema for training, which leverages the spatial and temporal locality of wide models to drastically reduce the amount of communication with GPU global memory. Comprehensive experiments show that cuWide can be up to more than 20× faster than the state-of-the-art GPU solutions and multi-core CPU solutions. © 2021 IEEE Show more
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publishedExternal links
Book title
2021 IEEE 37th International Conferecne on Data Engineering (ICDE)Volume
Pages / Article No.
Publisher
IEEEEvent
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