Performance Characterization of State-Of-The-Art Deep Learning Workloads on an IBM "Minsky" Platform

Date
2018-01-03
Authors
Guignard, Mauricio
Schild, Marcelo
Bederián, Carlos S.
Wolovick, Nicolás
Vega, Augusto J.
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Deep learning algorithms are known to demand significant computing horsepower, in particular when it comes to training these models. The capability of developing new algorithms and improving the existing ones is in part determined by the speed at which these models can be trained and tested. One alternative to attain significant performance gains is through hardware acceleration. However, deep learning has evolved into a large variety of models, including but not limited to fully-connected, convolutional, recurrent and memory networks. Therefore, it appears difficult that a single solution can provide effective acceleration for this entire deep learning ecosystem. This work presents detailed characterization results of a set of archetypal state-of-the-art deep learning workloads on a last-generation IBM POWER8 system with NVIDIA Tesla P100 GPUs and NVLink interconnects. The goal is to identify the performance bottlenecks (i.e. the accelerable portions) to provide a thorough study that can guide the design of prospective acceleration platforms in a more effective manner. In addition, we analyze the role of the GPU (as one particular type of acceleration engine) and its effectiveness as a function of the size of the problem.
Description
Keywords
Frontiers in AI and Software Engineering, DNN, Fathom Workload, IBM Minsky, performance characterization.
Citation
Extent
8 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 51st Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.