Paper Info
Title
DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications
Abstract
Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. This study aims to provide a detailed investigation of the I/O behavior of various scientific deep learning workloads running on the Theta supercomputer at Argonne Leadership Computing Facility. In this paper, we present DLIO, a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern scientific deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times by up to 6.7x.
Year
DOI
Venue
2021
10.1109/CCGrid51090.2021.00018
2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
Keywords
DocType
ISBN
deep learning,scientific applications,representative,benchmark,data intensive,I/O,characterization,Tensorflow,data pipeline
Conference
978-1-7281-9587-2
Citations 
PageRank 
References 
3
0.39
0
Authors
5
Name
Order
Citations
PageRank
Hariharan Devarajan142.42
Huihuo Zheng230.72
Anthony Kougkas342.76
Xian-he Sun41987182.64
Venkatram Vishwanath550747.27