Portable and Reusable Deep Learning Infrastructure with Containers to Accelerate Cancer Studies

被引：2

作者：

Zaki, George F. ^{[1
]}

Wozniak, Justin M. ^{[2
,3
]}

Ozik, Jonathan ^{[3
,4
]}

Collier, Nicholson ^{[3
,4
]}

Brettin, Thomas ^{[5
]}

Stevens, Rick ^{[3
,5
]}

机构：

[1] Frederick Natl Lab Canc Res, Frederick, MD 21701 USA

[2] Argonne Natl Lab, Data Sci & Learning, 9700 S Cass Ave, Argonne, IL 60439 USA

[3] Univ Chicago, Chicago, IL 60637 USA

[4] Argonne Natl Lab, Decis & Infrastruct Sci, 9700 S Cass Ave, Argonne, IL 60439 USA

[5] Argonne Natl Lab, Comp Environm & Life Sci, 9700 S Cass Ave, Argonne, IL 60439 USA

来源：

PROCEEDINGS OF 2018 IEEE/ACM 4TH INTERNATIONAL WORKSHOP ON EXTREME SCALE PROGRAMMING MODELS AND MIDDLEWARE (ESPM2 2018) | 2018年

关键词：

D O I：

10.1109/ESPM2.2018.00011

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Advanced programming models, domain specific languages, and scripting toolkits have the potential to greatly accelerate the adoption of high performance computing. These complex software systems, however, are often difficult to install and maintain, especially on exotic high-end systems. We consider deep learning workflows used on petascale systems and redeployment on research clusters using containers. Containers are used to deploy the MPI-based infrastructure, but challenges in efficiency, usability, and complexity must be overcome. In this work, we address these challenges through enhancements to a unified workflow system that manages interaction with the container abstraction, the cluster scheduler, and the programming tools. We also report results from running the application on our system, harnessing 298 TFLOPS (single precision).

引用

页码：54 / 61

页数：8

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