%0 Conference Paper %B Platform for Advanced Scientific Computing Conference (PASC20) %D 2020 %T Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications %A Qinglei Cao %A Yu Pei %A Kadir Akbudak %A Aleksandr Mikhalev %A George Bosilca %A Hatem Ltaief %A David Keyes %A Jack Dongarra %X Climate and weather can be predicted statistically via geospatial Maximum Likelihood Estimates (MLE), as an alternative to running large ensembles of forward models. The MLE-based iterative optimization procedure requires the solving of large-scale linear systems that performs a Cholesky factorization on a symmetric positive-definite covariance matrix---a demanding dense factorization in terms of memory footprint and computation. We propose a novel solution to this problem: at the mathematical level, we reduce the computational requirement by exploiting the data sparsity structure of the matrix off-diagonal tiles by means of low-rank approximations; and, at the programming-paradigm level, we integrate PaRSEC, a dynamic, task-based runtime to reach unparalleled levels of efficiency for solving extreme-scale linear algebra matrix operations. The resulting solution leverages fine-grained computations to facilitate asynchronous execution while providing a flexible data distribution to mitigate load imbalance. Performance results are reported using 3D synthetic datasets up to 42M geospatial locations on 130, 000 cores, which represent a cornerstone toward fast and accurate predictions of environmental applications. %B Platform for Advanced Scientific Computing Conference (PASC20) %I ACM %C Geneva, Switzerland %8 2020-06 %G eng %R https://doi.org/10.1145/3394277.3401846 %0 Conference Paper %B Workshop on Programming and Performance Visualization Tools (ProTools 19) at SC19 %D 2019 %T Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools %A Qinglei Cao %A Yu Pei %A Thomas Herault %A Kadir Akbudak %A Aleksandr Mikhalev %A George Bosilca %A Hatem Ltaief %A David Keyes %A Jack Dongarra %B Workshop on Programming and Performance Visualization Tools (ProTools 19) at SC19 %I ACM %C Denver, CO %8 2019-11 %G eng