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Remote sensing big data computing: Challenges and opportunities
As we have entered an era of high resolution earth observation, the RS data are undergoing
an explosive growth. The proliferation of data also give rise to the increasing complexity of …
an explosive growth. The proliferation of data also give rise to the increasing complexity of …
Use cases of lossy compression for floating-point data in scientific data sets
Architectural and technological trends of systems used for scientific computing call for a
significant reduction of scientific data sets that are composed mainly of floating-point data …
significant reduction of scientific data sets that are composed mainly of floating-point data …
Frontier: exploring exascale
As the US Department of Energy (DOE) computing facilities began deploying petascale
systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA …
systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA …
Optimizing error-bounded lossy compression for scientific data by dynamic spline interpolation
Today's scientific simulations are producing vast volumes of data that cannot be stored and
transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and …
transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and …
PKDGRAV3: beyond trillion particle cosmological simulations for the next era of galaxy surveys
We report on the successful completion of a 2 trillion particle cosmological simulation to z= 0
z=0 run on the Piz Daint supercomputer (CSCS, Switzerland), using 4000+ GPU nodes for a …
z=0 run on the Piz Daint supercomputer (CSCS, Switzerland), using 4000+ GPU nodes for a …
Counterfactual explanations for multivariate time series
Multivariate time series are used in many science and engineering domains, including
health-care, astronomy, and high-performance computing. A recent trend is to use machine …
health-care, astronomy, and high-performance computing. A recent trend is to use machine …
SDRBench: Scientific data reduction benchmark for lossy compressors
Efficient error-controlled lossy compressors are becoming critical to the success of today's
large-scale scientific applications because of the ever-increasing volume of data produced …
large-scale scientific applications because of the ever-increasing volume of data produced …
Cusz: An efficient gpu-based error-bounded lossy compression framework for scientific data
Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC
applications because it not only significantly reduces storage overhead but also can retain …
applications because it not only significantly reduces storage overhead but also can retain …
cuszp: An ultra-fast gpu error-bounded lossy compression framework with optimized end-to-end performance
Modern scientific applications and supercomputing systems are generating large amounts of
data in various fields, leading to critical challenges in data storage footprints and …
data in various fields, leading to critical challenges in data storage footprints and …
Adaptive techniques for clustered N-body cosmological simulations
ChaNGa is an N-body cosmology simulation application implemented using Charm++. In
this paper, we present the parallel design of ChaNGa and address many challenges arising …
this paper, we present the parallel design of ChaNGa and address many challenges arising …