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20182025
most citedLessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers

5 citations · 10 across the 9 of their papers we have counts for

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13 papers · 1 filter

cs.DC2025

Easy Acceleration with Distributed Arrays

Jeremy Kepner, Chansup Byun, LaToya Anderson +20

High level programming languages and GPU accelerators are powerful enablers for a wide range of applications. Achieving scalable vertical (within a compute node), horizontal (acros…

cs.DC2024

GPU Sharing with Triples Mode

Chansup Byun, Albert Reuther, LaToya Anderson +19

There is a tremendous amount of interest in AI/ML technologies due to the proliferation of generative AI applications such as ChatGPT. This trend has significantly increased demand…

cs.DC2024

Hypersparse Traffic Matrices from Suricata Network Flows using GraphBLAS

Michael Houle, Michael Jones, Dan Wallmeyer +8

Hypersparse traffic matrices constructed from network packet source and destination addresses is a powerful tool for gaining insights into network traffic. SuiteSparse: GraphBLAS,…

cs.DC2024

HPC with Enhanced User Separation

Andrew Prout, Albert Reuther, Michael Houle +19

HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and ra…

cs.DC2021

The MIT Supercloud Dataset

Siddharth Samsi, Matthew L Weiss, David Bestor +24

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers a…

cs.DC2020

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…