On The Coupling between HPC and Statistics: Challenges, Opportunities, and Future Trends of Emerging Techniques -- (Virtual SIAM CSE21 Minisymposium)

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KAUST

Abstract

Big data modeling/inference and large-scale simulations have followed largely independent paths to the high-performance computing (HPC) frontier, but important opportunities now arise that can be addressed by combining the strengths of each. HPC is becoming increasingly significant in scaling existing statistical methods to larger and more complex applications and developing novel methods that are amenable to scaling within the constraints that exist in modern HPC architectures. The purpose of this minisymposium is to bring together researchers in the area of statistics and HPC to review the challenges, opportunities, and recent progress towards exploiting current HPC technologies in accelerating applications related to applied statistics. In part I of this minisymposium, we aim to define the rendezvous point between HPC and statistical applications and existing challenges. Part II is more related to opportunities that could influence the effectiveness of this participatory relationship.

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SIAM CSE21 will run virtually with live sessions. 

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Registration is required to gain access to the online platform.

Please use your personal login information (sent by email) to join the online virtual platform (vFairs) and attend this exciting and timely minisymposium.

All times are listed in the CST time zone (+9hrs in Arabic Standard Time).

Talks are 20min including 5min Q&A.

MS21 (PART I) - March 1st 2021: 9:45 AM - 11:25 AM
9:45-10:00 ExaGeoStat: Moving Towards Large-Scale Geostatistical Modeling on Manycore Systems (abstract, slides)

Sameh Abdulah, King Abdullah University of Science & Technology (KAUST), Saudi Arabia.

10:05-10:20 Parallel Hierarchical Matrix Technique to Approximate Large Covariance Matrices, Likelihood Functions and Parameter Identification (abstract, slides)

Alexander Litvinenko, RWTH Aachen University, Germany.

10:25-10:40 Mixed Precision Numerical Linear Algebra for Statistics Computations (abstract, slides)

Nicholas J. Higham, University of Manchester, United Kingdom.

10:45-11:00 Accelerating Geostatistical Modeling and Prediction with Mixed-Precision Computations: A High-Productivity Approach with PaRSEC (abstract, slides)

Yu pei, University of Tennessee, Knoxville, U.S.

11:05-11:20 PbdR: Harnessing the Power of Supercomputers for R Workflows (abstract, slides)

George Ostrouchov, Oak Ridge National Laboratory, U.S.

MS56 (PART II) - March 1st 2021: 2:15 PM - 3:55 PM
2:15-2:30 Graphical Gaussian Process Models for Multivariate Spatial Data (abstract, slides)

Abhirup Datta, Johns Hopkins University, U.S.

2:35-2:50 A Bayesian Group Sparsity and Smoothing Regularization Method on Large Graphs (abstract, slides)

Huiyan Sang, Texas A&M University, U.S.

2:55-3:10 A Hybrid Parallel Framework of the Multi-Resolution Approximation for Massive Spatial Data (abstract, slides)

Huang Huang, King Abdullah University of Science & Technology (KAUST), Saudi Arabia.

3:15-3:30 Sum of Kronecker Products Representation and Its Cholesky Factorization for Spatial Covariance Matrices from Large Grids (abstract, slides)

Jian Cao, King Abdullah University of Science & Technology (KAUST), Saudi Arabia.

3:35-3:50 Parallel Cross-Validation: a Scalable Fitting Method for Gaussian Process Models (abstract, slides)

Douglas Nychka, National Center for Atmospheric Research, U.S.; Florian Gerber, Colorado School of Mines, U.S.