UGACHE: A Unified GPU Cache for Embedding-based Deep Learning

Author:

Song Xiaoniu12ORCID,Zhang Yiwen1ORCID,Chen Rong12ORCID,Chen Haibo1ORCID

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Shanghai AI Laboratory, Shanghai, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

HighTech Support Program from Shanghai Committee of Science and Technology

Publisher

ACM

Reference49 articles.

1. 2011. Peer-to-Peer and Unified Virtual Addressing. https://developer.download.nvidia.com/CUDA/training/cuda_webinars_GPUDirect_uva.pdf. 2011. Peer-to-Peer and Unified Virtual Addressing. https://developer.download.nvidia.com/CUDA/training/cuda_webinars_GPUDirect_uva.pdf.

2. 2021. Open Graph Benchmark: Benchmark datasets data loaders and evaluators for graph machine learning. https://ogb.stanford.edu/. 2021. Open Graph Benchmark: Benchmark datasets data loaders and evaluators for graph machine learning. https://ogb.stanford.edu/.

3. 2021. Open Graph Benchmark: The MAG240M dataset. https://ogb.stanford.edu/docs/lsc/mag240m/. 2021. Open Graph Benchmark: The MAG240M dataset. https://ogb.stanford.edu/docs/lsc/mag240m/.

4. 2021. Open Graph Benchmark: The ogbn-papers100M dataset. https://ogb.stanford.edu/docs/nodeprop/#ogbn-papers100M. 2021. Open Graph Benchmark: The ogbn-papers100M dataset. https://ogb.stanford.edu/docs/nodeprop/#ogbn-papers100M.

5. 2022. Distributed Embeddings · NVIDIA-Merlin. https://github.com/NVIDIA-Merlin/distributed-embeddings 2022. Distributed Embeddings · NVIDIA-Merlin. https://github.com/NVIDIA-Merlin/distributed-embeddings

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