Communication Avoiding Algorithms for Linear Algebra and Beyond

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=_FqYHIsuunw


Duration: 27:42
332 views
5

Jim Demmel, UC Berkeley
Parallel and Distributed Algorithms for Inference and Optimization
http://simons.berkeley.edu/talks/jim-demmel-2013-10-24



Other Videos By Simons Institute for the Theory of Computing


2013-11-30Limiting Behavior in Large Networks
2013-11-06How Hard Is It To Find A Good Solution?
2013-10-29Highly Scalable Branch-and-Bound for Maximum Monomial Agreement
2013-10-28Tall-and-skinny QR Factorizations in MapReduce
2013-10-28Parallel Active Learning
2013-10-28Three Goals in Parallel Graph Computations: High Performance, High Productivity, and Reduced Comm...
2013-10-28First Order Methods for Distributed Network Optimization
2013-10-28Stochastic Optimization for Big Data Machine Learning Problems
2013-10-28Large-Scale Generalized Matching
2013-10-28Consensus-Based Distributed Online Prediction and Optimization
2013-10-28Communication Avoiding Algorithms for Linear Algebra and Beyond
2013-10-28Using Data-Oblivious Algorithms for Private Cloud Storage Access
2013-10-28Parallelism in Linear and Mixed Integer Programming
2013-10-28Peeling Algorithms
2013-10-28Provably Efficient Algorithms for Training Deep Networks
2013-10-28Parallelization by Approximation
2013-10-28Randomized Asynchronous Linear Solvers
2013-10-28An Algebraic View of the New Randomized Kaczmarz Linear Solver
2013-10-28Parallel Coordinate Descent Methods for Big Data Optimization
2013-10-28Distributed Learning, Communication Complexity, and Privacy
2013-10-28On the O(1/k) Convergence of the Asynchronous Distributed Alternating Direction Method of Mult...


Tags:
Simons Institute
Big Data
UC Berkeley
computer science
theory of computing
Jim Demmel