Publicity
Accessible news articles about our research
Accessible news articles about our research
- Here is an accessible article about the GraphBLAS effort (accompanied with three unpleasant pictures). More info, including a reference implementation by Tim Davis and the C language API can be found in the GraphBLAS Forum website (standard building blocks for graph algorithms in the language of matrices).
- HipMCL clusters protein similarity networks with 70 billion edges in a couple of hours. This highly-scalable implementation of the Markov Cluster algorithm is available open source. Here is an accessible press release.
- Communication-avoiding algorithms help accelerate graphical model estimation (a.k.a. inverse covariance matrix estimation): http://cs.lbl.gov/news-media/news/2018/scalable-machine-learning-with-hp-concord/
A few presentations with accompanying videos
A few presentations with accompanying videos
- The COVID-19 reality encouraged us to create a Youtube channel for our research presentations.
- Communication-Avoiding Sparse Matrix Algorithms for Large Graph and Machine Learning Problems, New Architectures and Algorithms Workshop at IPAM (UCLA), November 2018. [Video] [Slides]
- Communication-Avoiding Sparse-Matrix Primitives for Parallel Machine Learning, Sparse Days, Toulouse, September 2018. [Video] [Slides]
- Genomics, Graphs and the GraphBLAS, GraphXD: Graphs Across Domains, Berkeley, April 2018. [Video] [Slides]
- Reducing Communication in Parallel Graph Computations, MMDS, Berkeley, June 2014. [Video] [Slides]
- Three Goals in Parallel Graph Computations: High Performance, High Productivity, and Reduced Communication, Simons Institute for Theory of Computing, October 2013, [Video] [Slides]