Add to ORKGAs simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solvers that couple vast numbers of degrees of freedom, must span a widening gap between ambitious applications and austere architectures to support them. We present fifteen universals for researchers in scalable solvers: imperatives from computer architecture that scalable solvers must respect, strategies towards achieving them that are currently well established, and additional strategies currently being developed for an effective and efficient exascale software ecosystem. We consider recent generalizations of what it means to “solve” a computational problem, which suggest that we have often been “oversolving” them at the smaller scales of the ...
With the ability to collect and store increasingly large datasets on modern computers comes the need...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
In engineering and other fields, it is common to use a computer simulation to model a real world pro...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
In this talk, we will look at the current state of high performance computing and look at the next s...
Massive high-dimensional data sets are ubiquitous in all scientific disciplines. Extracting meaningf...
Kernel methods are a popular technique for extending linear models to handle non-linear spatial prob...
Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit...
In statistics one can distinguish three cases: 1) datasets where the number of dimensions is many ti...
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data”...
Random field Spatial process Central limit theorem Uniform law of large numbers Law of large numbers...
With the ability to collect and store increasingly large datasets on modern computers comes the need...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
In engineering and other fields, it is common to use a computer simulation to model a real world pro...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
As simulation and analytics enter the exascale era, numerical algorithms, particularly implicit solv...
In this talk, we will look at the current state of high performance computing and look at the next s...
Massive high-dimensional data sets are ubiquitous in all scientific disciplines. Extracting meaningf...
Kernel methods are a popular technique for extending linear models to handle non-linear spatial prob...
Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit...
In statistics one can distinguish three cases: 1) datasets where the number of dimensions is many ti...
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data”...
Random field Spatial process Central limit theorem Uniform law of large numbers Law of large numbers...
With the ability to collect and store increasingly large datasets on modern computers comes the need...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
In engineering and other fields, it is common to use a computer simulation to model a real world pro...