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20162026
most citedAI and ML Accelerator Survey and Trends

96 citations · 309 across the 37 of their papers we have counts for

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Showing 2017 · cs.DCShow all

5 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

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

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

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

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…