Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis

Interactive massively parallel computations are critical for machine learning and data analysis. These computations are a staple of the MIT Lincoln Laboratory Supercomputing Center (LLSC) and has required the LLSC to develop unique interactive supercomputing capabilities. Scaling interactive machine...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Reuther, Albert, Kepner, Jeremy, Byun, Chansup, Samsi, Siddharth, Arcand, William, Bestor, David, Bergeron, Bill, Gadepally, Vijay, Houle, Michael, Hubbell, Matthew, Jones, Michael, Klein, Anna, Milechin, Lauren, Mullen, Julia, Prout, Andrew, Rosa, Antonio, Yee, Charles, Michaleas, Peter
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Data analysis
Machine learning
Task scheduling
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Interactive massively parallel computations are critical for machine learning and data analysis. These computations are a staple of the MIT Lincoln Laboratory Supercomputing Center (LLSC) and has required the LLSC to develop unique interactive supercomputing capabilities. Scaling interactive machine learning frameworks, such as TensorFlow, and data analysis environments, such as MATLAB/Octave, to tens of thousands of cores presents many technical challenges - in particular, rapidly dispatching many tasks through a scheduler, such as Slurm, and starting many instances of applications with thousands of dependencies. Careful tuning of launches and prepositioning of applications overcome these challenges and allow the launching of thousands of tasks in seconds on a 40,000-core supercomputer. Specifically, this work demonstrates launching 32,000 TensorFlow processes in 4 seconds and launching 262,000 Octave processes in 40 seconds. These capabilities allow researchers to rapidly explore novel machine learning architecture and data analysis algorithms.
ISSN:2331-8422
DOI:10.48550/arxiv.1807.07814