activity
20172020
most citedOn the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

9 citations · 13 across the 3 of their papers we have counts for

collaborators

5 papers

cs.DC20204 cited

MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction

Xin Liang, Ben Whitney, Jieyang Chen +8

Data management is becoming increasingly important in dealing with the large amounts of data produced by large-scale scientific simulations and instruments. Existing multilevel com…

cs.DC2020

Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs

Jieyang Chen, Lipeng Wan, Xin Liang +8

Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific…

math.NA2020

Towards 1ULP evaluation of Daubechies Wavelets

Nicholas Thompson, John Maddock, George Ostrouchov +3

We present algorithms to numerically evaluate Daubechies wavelets and scaling functions to high relative accuracy. These algorithms refine the suggestion of Daubechies and Lagarias…

physics.plasm-ph2018

A tight-coupling scheme sharing minimum information across a spatial interface between gyrokinetic turbulence codes

Julien Dominski, Seung-Hoe Ku, Choong-Seock Chang +5

A new scheme that tightly couples kinetic turbulence codes across a spatial interface is introduced. This scheme evolves from considerations of competing strategies and down-select…

cs.PF20179 cited

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

Axel Huebl, Rene Widera, Felix Schmitt +5

We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for a…