Abstract: In this talk I will present novel high performance algorithmic techniques and data structures to build a scalable sparse tensor library and a benchmark suite on multicore CPUs and graphics co-processors (GPUs). A tensor could be regarded as a multiway array, generalizing matrices to more than two dimensions. When used to represent multifactor data, tensor methods can help analysts discover latent structure; this capability has found numerous applications in data modeling and mining in such domains as healthcare analytics, social networks analytics, computer vision, signal processing, and neuroscience, to name a few. Besides, sparse tensor algebra has been found useful in more applications, such as Quantum Chemistry and Deep Learning. This talk will cover my recently proposed performance-efficient and space-saving sparse tensor format (named “HiCOO”), based-on which a sparse tensor library (named “HiParTi”) and a sparse tensor benchmark suite (named “PASTA”) are built. The future directions of tensors and their influence on applications and computer architectures will be illustrated along with recent trends.

Short Bio: Jiajia Li is a research scientist in High Performance Computing group at Pacific Northwest National Laboratory (PNNL). She has received her Ph.D. degree from Georgia Institute of Technology in 2018. Her current research emphasizes on optimizing tensor methods especially for sparse data from diverse applications by utilizing various parallel architectures. She is an awardee of Best Student Paper Award at SC’18, Best Paper Finalist at PPoPP’19, and “A Rising Star in Computational and Data Sciences”. She has served on the technical program committee of conferences, such as PPoPP, SC, ICS, IPDPS, ICPP, HiPC, Euro-Par. In the past, she had received a Ph.D. degree from Institute of Computing Technology at Chinese Academy of Sciences, China and a B.S. degree in Computational Mathematics from Dalian University of Technology, China. Please check her website for more information: http://jiajiali.org .