Accelerating Large-Scale Deep Learning Tasks

PhD Thesis Proposal Defence


Title: "Accelerating Large-Scale Deep Learning Tasks"

by

Mr. Lipeng WANG


Abstract:

Large-scale deep learning tasks usually run on parallel and distributed 
frameworks such as TensorFlow and PyTorch, and take hours to days to 
obtain training results. These frameworks utilize hardware accelerators, 
especially GPUs, to speed up the computation. However, data access and 
processing in these tasks takes a significant amount of time. Therefore, 
we propose to accelerate these tasks by improving their dataset storage 
and processing. Specifically, we develop DIESEL, a scalable dataset 
storage and caching system that runs between a training framework and the 
underlying distributed file system. The main features of DIESEL include 
metadata snapshot, per-task distributed cache, and chunk-based storage and 
shuffle. Furthermore, we optimize a GPU-assisted image decoding method for 
training tasks on image datasets. Our experiments on real-world training 
tasks show that (1) DIESEL halves the data access time and reduces the 
training time by around 15%-27%, and (2) our optimized image decoding 
method is 30%-50% faster than existing GPU-accelerated image decoding 
libraries.


Date:			Friday, 4 September 2020

Time:                  	10:00am - 12:00noon

Zoom Meeting:           https://hkust.zoom.us/j/99015360724

Committee Members:	Dr. Qiong Luo (Supervisor)
  			Dr. Kai Chen (Chairperson)
 			Dr. Qifeng Chen
  			Dr. Wei Wang


**** ALL are Welcome ****