activity
20142026
most citedInteractive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis

272 citations · 1.7k across the 81 of their papers we have counts for

collaborators
Showing 2020Show all

9 papers · 1 filter

cs.DC2020★ 162 cited

Survey of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones +3

New machine learning accelerators are being announced and released each month for a variety of applications from speech recognition, video object detection, assisted driving, and m…

cs.LG2020

Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid

Andrew C. Kirby, Siddharth Samsi, Michael Jones +3

A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computatio…

cs.CV2020★ 3 cited

Accuracy and Performance Comparison of Video Action Recognition Approaches

Matthew Hutchinson, Siddharth Samsi, William Arcand +16

Over the past few years, there has been significant interest in video action recognition systems and models. However, direct comparison of accuracy and computational performance re…

cs.DC2020★ 2 cited

Benchmarking network fabrics for data distributed training of deep neural networks

Siddharth Samsi, Andrew Prout, Michael Jones +16

Artificial Intelligence/Machine Learning applications require the training of complex models on large amounts of labelled data. The large computational requirements for training de…

cs.DC2020★ 5 cited

Best of Both Worlds: High Performance Interactive and Batch Launching

Chansup Byun, Jeremy Kepner, William Arcand +16

Rapid launch of thousands of jobs is essential for effective interactive supercomputing, big data analysis, and AI algorithm development. Achieving thousands of launches per second…

cs.NI2020★ 12 cited

Multi-Temporal Analysis and Scaling Relations of 100,000,000,000 Network Packets

Jeremy Kepner, Chad Meiners, Chansup Byun +23

Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network tr…

cs.LG2020★ 11 cited

GraphChallenge.org Sparse Deep Neural Network Performance

Jeremy Kepner, Simon Alford, Vijay Gadepally +5

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalabil…

cs.DC2020★ 9 cited

GraphChallenge.org Triangle Counting Performance

Siddharth Samsi, Jeremy Kepner, Vijay Gadepally +9

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been…

cs.DC2020★ 12 cited

75,000,000,000 Streaming Inserts/Second Using Hierarchical Hypersparse GraphBLAS Matrices

Jeremy Kepner, Tim Davis, Chansup Byun +16

The SuiteSparse GraphBLAS C-library implements high performance hypersparse matrices with bindings to a variety of languages (Python, Julia, and Matlab/Octave). GraphBLAS provides…