Reshaping geostatistical modeling and prediction for extreme-scale environmental applications
We extend the capability of space-time geostatistical modeling using algebraic
approximations, illustrating application-expected accuracy worthy of double precision from …
approximations, illustrating application-expected accuracy worthy of double precision from …
Accelerating geostatistical modeling and prediction with mixed-precision computations: A high-productivity approach with parsec
Geostatistical modeling, one of the prime motivating applications for exascale computing, is
a technique for predicting desired quantities from geographically distributed data, based on …
a technique for predicting desired quantities from geographically distributed data, based on …
Meeting the real-time challenges of ground-based telescopes using low-rank matrix computations
Adaptive Optics (AO) is a technology that permits to measure and mitigate the distortion
effects of atmospheric turbulence on optical beams. AO must operate in real-time by …
effects of atmospheric turbulence on optical beams. AO must operate in real-time by …
Solving acoustic boundary integral equations using high performance tile low-rank LU factorization
We design and develop a new high performance implementation of a fast direct LU-based
solver using low-rank approximations on massively parallel systems. The LU factorization is …
solver using low-rank approximations on massively parallel systems. The LU factorization is …
HPAC: evaluating approximate computing techniques on HPC OpenMP applications
As we approach the limits of Moore's law, researchers are exploring new paradigms for
future high-performance computing (HPC) systems. Approximate computing has gained …
future high-performance computing (HPC) systems. Approximate computing has gained …
A framework to exploit data sparsity in tile low-rank cholesky factorization
We present a general framework that couples the PaRSEC runtime system and the HiCMA
numerical library to solve challenging 3D data-sparse problems. Though formally dense …
numerical library to solve challenging 3D data-sparse problems. Though formally dense …
The Template Task Graph (TTG)-an emerging practical dataflow programming paradigm for scientific simulation at extreme scale
We describe TESSE, an emerging general-purpose, open-source software ecosystem that
attacks the twin challenges of programmer productivity and portable performance for …
attacks the twin challenges of programmer productivity and portable performance for …
Accelerating seismic redatuming using tile low-rank approximations on NEC SX-Aurora TSUBASA
With the aim of imaging subsurface discontinuities, seismic data recorded at the surface of
the Earth must be numerically re-positioned at locations in the subsurface where reflections …
the Earth must be numerically re-positioned at locations in the subsurface where reflections …
Leveraging parsec runtime support to tackle challenging 3d data-sparse matrix problems
The task-based programming model associated with dynamic runtime systems has gained
popularity for challenging problems because of workload imbalance, heterogeneous …
popularity for challenging problems because of workload imbalance, heterogeneous …
High performance multivariate geospatial statistics on manycore systems
Modeling and inferring spatial relationships and predicting missing values of environmental
data are some of the main tasks of geospatial statisticians. These routine tasks are …
data are some of the main tasks of geospatial statisticians. These routine tasks are …