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DTSTAMP:20210916T132447Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210709T123000
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UID:submissions.pasc-conference.org_PASC21_sess188_pap_jun101@linklings.co
m
SUMMARY:Extreme-Scale Task-Based Cholesky Factorization Toward Climate and
Weather Prediction Applications
DESCRIPTION:Paper\n\nExtreme-Scale Task-Based Cholesky Factorization Towar
d Climate and Weather Prediction Applications\n\nCao, Pei, Akbudak, Mikhal
ev, Bosilca...\n\nClimate and weather can be predicted statistically via g
eospatial Maximum Likelihood Estimates (MLE), as an alternative to running
large ensembles of forward models. The MLE-based iterative optimization p
rocedure requires factorization of large-scale linear systems that consist
of a symmetric positive-definite covariance matrix-a demanding dense fact
orization in terms of memory footprint and computation. We propose a novel
solution to this problem that results in fast and accurate predictions: a
t the mathematical level, we reduce the computational requirement by explo
iting data sparsity structure of the matrix off-diagonal tiles by means of
low-rank approximations; and, at the programming-paradigm level, we integ
rate PaRSEC, a dynamic, task-based runtime to reach unparalleled levels of
efficiency for solving extreme-scale problems for environmental applicati
ons. The resulting solution leverages fine-grained computations to facilit
ate asynchronous execution while providing flexible data distribution to m
itigate load imbalance. Performance results are reported using 3D datasets
up to 42M geospatial locations on 130,000 cores.\n\nDomain: CS and Math,
Climate and Weather
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