Since May 2020, we try to post some of our new talks to the PASSION Lab YouTube channel. Some older videos can also be found from the links here.
Selected Talks
[1] | GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU. ACS HPC and Data Analytics Workshop, Baltimore, MD, 2019 [Slides] |
[2] | GraphBLAS and parallelism in machine learning & computational genomics. LBNL CS Area Review, Berkeley, CA, 2019 [Slides] |
[3] | GraphBLAS in Data Analytics and Machine Learning. ACS Reverse Site Visit, Berkeley, CA, 2019 [Slides] |
[4] | Communication-Avoiding Sparse Matrix Algorithms for Large Graph and Machine Learning Problems. SIAM CSE, Spokane, WA, 2019 [Slides] |
[5] | Large-scale parallel computing for computational genomics. H.Bioinfo-11, Thessaloniki, Greece, 2018 [Slides] |
[6] | Communication-Avoiding Sparse Matrix Primitives for Parallel Machine Learning. Sparse Days, Toulouse, France, 2018 [Slides] |
[7] | Scaling Parallel Graph Analysis & Machine Learning using Sparse Matrix Operations. Seminar at Michigan State University and NERSC, 2018 [Slides] |
[8] | Graph algorithms, computational motifs,and GraphBLAS. Exascale Computing Project 2nd Annual Meeting, Exagraph Tutorial Session, Knoxville, TN, 2018 [Slides] |
[9] | Parallel Algorithms across the GraphBLAS Stack. ACS HPC and Data Analytics Workshop, Baltimore, MD, 2017 [Slides] |
[10] | 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] |
[11] | Parallel de novo Assembly of Complex (Meta) Genomes via HipMer. HiCOMB @ IPDPS, Chicago, IL, 2016 [Slides] |
[12] | Scalable algorithms for genome assembly, alignment, and genetic mapping. Georgia Institute of Technology, School of Computational Science and Engineering, Atlanta, GA, 2015 [Slides] |
[13] | Distributed-Memory Parallel Algorithms for Graph Traversal and Genome Assembly. SUNY Stony Brook, SUNY Albany, SUNY Buffalo , NY, 2014 [Slides] |
[14] | The Graph BLAS effort and its implications for Exascale. SIAM Workshop on Exascale Applied Mathematics Challenges and Opportunities (EX14) , Chicago, IL, 2014 [Slides] |
[15] | Reducing Communication in Parallel Graph Computations. Workshop on Algorithms for Modern Massive Data Sets (MMDS) , Berkeley, CA, 2014 [Slides | Video] |
[16] | High-Productivity and High- Performance Analysis of Filtered Semantic Graphs. SIAM Conference on Parallel Computing , Portland, OR, 2014 [Slides] |
[17] | Three Goals in Parallel Graph Computations: High Performance, High Productivity, and Reduced Communication. Seminar at the Simons Institute , Berkeley, CA, 2013. [Video and Slides] |
[18] | A sustainable software stack for parallel graph analysis. Discovery 2015: HPC and Cloud Computing Workshop, Berkeley, CA, 2012 [slides] |
[19] | Parallel algorithms for sparse matrix product, indexing, and assignment. In Scientific Computing and Matrix Computations Seminar, UC Berkeley 2012. [slides] |
[20] | Parallel Breadth-First Search on Distributed Memory Systems. In Supercomputing, Seattle 2011. [slides] |
[21] | An Overview of the Combinatorial BLAS and Knowledge Discovery Toolbox. IBM Exascale Analytics Discussion, 2011. [slides] |
[22] | Scalable Parallel Primitives for Massive Graph Computation. Invited talk at Sandia National Labs, Lawrence Berkeley National Lab, and Argonne National Lab, 2010. [slides] |
[23] | Parallel Sparse Matrix-Vector and Matrix-Transpose-Vector Multiplication Using Compressed Sparse Blocks. In ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) , Calgary, Canada, 2009. [slides] |
[24] | Challenges and advances in parallel sparse matrix-matrix multiplication. In International Conference on Parallel Processing (ICPP), Portland, 2008. [slides] |
[25] | Gaussian Elimination Based Algorithms on the GPU. In International Workshop on Parallel Matrix Algorithms and Applications (PMAA), Neuchâtel, Switzerland, 2008. [slides] |