activity
20172022
most citedAI and ML Accelerator Survey and Trends

96 citations · 176 across the 9 of their papers we have counts for

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15 papers · 1 filter

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

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.DC2020

Fast Mapping onto Census Blocks

Jeremy Kepner, Andreas Kipf, Darren Engwirda +21

Pandemic measures such as social distancing and contact tracing can be enhanced by rapidly integrating dynamic location data and demographic data. Projecting billions of longitude…

cs.DC2020

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

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…

cs.DC2019

Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M

Jeremy Kepner, Vijay Gadepally, Lauren Milechin +15

The Dynamic Distributed Dimensional Data Model (D4M) library implements associative arrays in a variety of languages (Python, Julia, and Matlab/Octave) and provides a lightweight i…