[1]

CommunicationAvoiding Sparse Matrix Primitives for Parallel Machine Learning.
Sparse Days, Toulouse, France, 2018
[Slides]

[2]

Scaling Parallel Graph Analysis & Machine Learning using Sparse Matrix Operations.
Seminar at Michigan State University and NERSC, 2018
[Slides]

[3]

Graph algorithms, computational motifs,and GraphBLAS.
Exascale Computing Project 2nd Annual Meeting, Exagraph Tutorial Session, Knoxville, TN, 2018
[Slides]

[4]

Parallel Algorithms across the GraphBLAS Stack.
ACS HPC and Data Analytics Workshop, Baltimore, MD, 2017
[Slides]

[5]

Concepts in the GraphBLAS API.
Various Places, 2017
[Slides]

[6]

Faster parallel Graph BLAS kernels and new graph algorithms in matrix algebra.
EECS, UC Berkeley, 2016
[2016 Slides] and
HP Labs, Palo Alto, CA, 2015
[2015 Slides]

[7]

Parallel de novo Assembly of Complex (Meta) Genomes via HipMer.
HiCOMB @ IPDPS, Chicago, IL, 2016
[Slides]

[8]

Scalable algorithms for genome assembly, alignment, and genetic mapping.
Georgia Institute of Technology, School of Computational Science and Engineering, Atlanta, GA, 2015
[Slides]

[9]

Parallel Graph Algorithms.
Guest lecture at CS267, UC Berkeley 20122016.
[2015 video],
[2014 video],
[2013 video],
[2012 video]

[10]

DistributedMemory Parallel Algorithms for Graph Traversal and
Genome Assembly.
SUNY Stony Brook, SUNY Albany, SUNY Buffalo , NY, 2014
[Slides]

[11]

The Graph BLAS effort and its implications for Exascale.
SIAM Workshop on Exascale Applied Mathematics Challenges and Opportunities (EX14) , Chicago, IL, 2014
[Slides]

[12]

Reducing Communication in Parallel Graph Computations.
Workshop on Algorithms for Modern Massive Data Sets (MMDS) , Berkeley, CA, 2014
[Slides 
Video]

[13]

HighProductivity and High Performance Analysis of Filtered Semantic Graphs.
SIAM Conference on Parallel Computing , Portland, OR, 2014
[Slides]

[14]

Three Goals in Parallel Graph Computations: High Performance, High Productivity, and Reduced Communication.
Seminar at the Simons Institute , Berkeley, CA, 2013.
[Video and Slides]

[15]

A sustainable software stack for parallel graph analysis.
Discovery 2015: HPC and Cloud Computing Workshop, Berkeley, CA, 2012
[slides]

[16]

Parallel algorithms for sparse matrix product, indexing, and assignment.
In Scientific Computing and Matrix Computations Seminar, UC Berkeley 2012.
[slides]

[17]

Parallel BreadthFirst Search on Distributed Memory Systems.
In Supercomputing, Seattle 2011.
[slides]

[18]

An Overview of the Combinatorial BLAS and Knowledge Discovery Toolbox.
IBM Exascale Analytics Discussion, 2011.
[slides]

[19]

Scalable Parallel Primitives for Massive Graph Computation.
Invited talk at Sandia National Labs, Lawrence Berkeley National Lab, and Argonne National Lab, 2010.
[slides]

[20]

Parallel Sparse MatrixVector and MatrixTransposeVector Multiplication Using Compressed Sparse Blocks.
In ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) , Calgary, Canada, 2009.
[slides]

[21]

Challenges and advances in parallel sparse matrixmatrix
multiplication.
In International Conference on Parallel Processing
(ICPP), Portland, 2008.
[slides]

[22]

Gaussian Elimination Based Algorithms on the GPU.
In International Workshop on Parallel Matrix Algorithms
and Applications (PMAA), Neuchâtel, Switzerland, 2008.
[slides]
