High-Level Data Abstraction and Elastic Data Caching for Data-Intensive AI Applications on Cloud-Native Platforms
Author:
Affiliation:
1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
2. Alibaba Group, Hangzhou, Zhejiang, China
3. Alluxio. Inc., San Mateo, CA, USA
Funder
National Natural Science Foundation of China
Jiangsu Province Science and Technology Key Program
Fundamental Research Funds for the Central Universities
Collaborative Innovation Center of Novel Software Technology and Industrialization
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computational Theory and Mathematics,Hardware and Architecture,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/71/10248992/10249214.pdf?arnumber=10249214
Reference41 articles.
1. Characterizing Deep-Learning I/O Workloads in TensorFlow
2. DIESEL: A Dataset-Based Distributed Storage and Caching System for Large-Scale Deep Learning Training
3. Enabling Efficient Large-Scale Deep Learning Training with Cache Coherent Disaggregated Memory Systems
4. Hoard: A distributed data caching system to accelerate deep learning training on the cloud;pinto;Comput Res Repository,2018
5. Entropy-Aware I/O Pipelining for Large-Scale Deep Learning on HPC Systems
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