activity
19932026
most citedInteractive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis

272 citations · 3.4k across the 163 of their papers we have counts for

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Showing 2017 · cs.DCShow all

7 papers · 2 filters

cs.DC2017★ 68 cited

Streaming Graph Challenge: Stochastic Block Partition

Edward Kao, Vijay Gadepally, Michael Hurley +9

An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition prob…

cs.DC2017★ 77 cited

Static Graph Challenge: Subgraph Isomorphism

Siddharth Samsi, Vijay Gadepally, Michael Hurley +9

The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. Th…

cs.DC2017★ 37 cited

Enabling Massive Deep Neural Networks with the GraphBLAS

Jeremy Kepner, Manoj Kumar, José Moreira +3

Deep Neural Networks (DNNs) have emerged as a core tool for machine learning. The computations performed during DNN training and inference are dominated by operations on the weight…

cs.DC2017★ 7 cited

Performance Measurements of Supercomputing and Cloud Storage Solutions

Michael Jones, Jeremy Kepner, William Arcand +10

Increasing amounts of data from varied sources, particularly in the fields of machine learning and graph analytics, are causing storage requirements to grow rapidly. A variety of t…

cs.DC2017★ 14 cited

MIT SuperCloud Portal Workspace: Enabling HPC Web Application Deployment

Andrew Prout, William Arcand, David Bestor +13

The MIT SuperCloud Portal Workspace enables the secure exposure of web services running on high performance computing (HPC) systems. The portal allows users to run any web applicat…

cs.DC2017★ 113 cited

Scalable System Scheduling for HPC and Big Data

Albert Reuther, Chansup Byun, William Arcand +8

In the rapidly expanding field of parallel processing, job schedulers are the "operating systems" of modern big data architectures and supercomputing systems. Job schedulers alloca…

cs.DC2017

D4M 3.0

Lauren Milechin, Alexander Chen, Vijay Gadepally +3

The D4M tool is used by hundreds of researchers to perform complex analytics on unstructured data. Over the past few years, the D4M toolbox has evolved to support connectivity with…