[SPCL_Bcast] Parallel Sparse Matrix Algorithms for Data Analysis and Machine Learning

Speaker: Aydın Buluç
Venue: SPCL_Bcast, recorded on 24 March, 2022
Abstract: In addition to the traditional theory and experimental pillars of science, we are witnessing the emergence of three additional pillars, which are simulation, data analysis, and machine learning. All three recent pillars of science rely on computing but in different ways. Matrices, and sparse matrices in particular, play an outsized role in all three computing related pillars of science, which will be the topic of my talk.
I will first highlight some of the emerging use cases of sparse matrices in data analysis and machine learning. These include graph computations, graph representation learning, and computational biology. The rest of my talk will focus on new parallel algorithms for such modern computations on sparse matrices. These include the use of "masking" for filtering out undesired output entries in sparse-times-sparse and dense-times-dense matrix multiplication, new distributed-memory algorithms for sparse matrix times tall-skinny dense matrix multiplication, combinations of these algorithms, and subroutines of them.