Franz Franchetti (Carnegie Mellon University)
Carnegie Mellon University
Publications by Franz Franchetti (Carnegie Mellon University)
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Spiral/AIML: Resource-Constrained Co-Optimization for High-Performance, Data-Intensive Computing
November 11, 2019 • Video
Scott McMillanFranz Franchetti (Carnegie Mellon University)
Watch SEI Principal Investigator, Dr. Scott McMillan, and research collaborator, CMU ECE Professor Franz Franchetti, discuss a community research effort to develop tools to reduce the prohibitive cost of implementing and re-implementing AI/ML software on
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Spiral/AIML: Frontiers of Graph Processing in Linear Algebra
October 28, 2019 • Poster
Scott McMillanFranz Franchetti (Carnegie Mellon University)
This poster describes research to use a linear algebraic approach to graph algorithms
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Spiral/AIML: Co-optimization for High-Performance, Data-Intensive Computing in Resource Constrained Environments
October 28, 2019 • Presentation
Scott McMillanFranz Franchetti (Carnegie Mellon University)
Data-intensive computing is pervasive. This presentation provides an update on research to allow platform developers to realize high-performance AI/ML applications on leading-edge hardware architectures faster and cheaper.
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Spiral/AIML: Frontiers of Graph Processing in Linear Algebra
October 28, 2019 • Poster
Scott McMillanFranz Franchetti (Carnegie Mellon University)
This poster represents research to extend the use of linear algebra beyond simple graph traversal.
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Automatic Code Generation for Graph Algorithms
October 23, 2018 • Poster
Scott McMillanFranz Franchetti (Carnegie Mellon University)
This poster describes automated code generation of high-performance libraries of graph algorithms, tuned for different hardware architectures.
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Mathematical Foundations of the GraphBLAS
September 15, 2016 • Conference Paper
Jeremy Kepner (MIT Lincoln Laboratory)Peter Aaltonen (Indiana University)David Bader (Georgia Institute of Technology)
This paper introduces the mathematics of the GraphBLAS, which is being developed to bring matrix-based graph algorithms to the broadest possible audience.
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