Corpus ID: 7534789

Spatial interpolation in massively parallel computing environments

@inproceedings{Hennebohl2011SpatialII,
  title={Spatial interpolation in massively parallel computing environments},
  author={K. Hennebohl and Marius Appel and E. Pebesma},
  year={2011}
}
Prediction of environmental phenomena at non-observed locations is a fundamental task in geographic information science. Often, samples are taken at a limited number of sensor locations and spatial and spatio-temporal interpolation is used to generate continuous maps. The computational cost of the underlying algorithms usually grows with the number of data entering the interpolation and the number of locations for which interpolated values are needed. Thus, real-time provision and processing of… Expand
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References

SHOWING 1-10 OF 21 REFERENCES
Efficient kriging for real-time spatio-temporal interpolation
TLDR
This work forms the kriging problem, uses an iterative solver (Saad, 2003), and accelerates the solver using fast summation algorithms like GPUML or FIGTREE to demonstrate substantial improvement in the performance of the approach. Expand
Towards personal high-performance geospatial computing (HPC-G): perspectives and a case study
TLDR
This work considers Personal HPC-G possesses many favorable features: low initial and operational costs, good support for data management and excellent support for both numeric modeling and interactive visualization, and compares it with traditional Cluster computing and the newly emerging Cloud computing. Expand
Design and performance evaluation of snow cover computing on GPUs
TLDR
This article demonstrates how to deploy the CUDA architecture, which utilizes the powerful parallel computation capacity of GPU, to accelerate computational process of snow cover depth using the inverse-distance weighting (IDW) method. Expand
Geospatial Cyberinfrastructure: Past, present and future
TLDR
This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Expand
GPUML : Graphical processors for speeding up kernel machines
Algorithms based on kernel methods play a central role in statistical machine learning. At their core are a number of linear algebra operations on matrices of kernel functions which take as argumentsExpand
GPU Computing
TLDR
The background, hardware, and programming model for GPU computing is described, the state of the art in tools and techniques are summarized, and four GPU computing successes in game physics and computational biophysics that deliver order-of-magnitude performance gains over optimized CPU applications are presented. Expand
Programming Massively Parallel Processors. A Hands-on Approach
  • Jie Cheng
  • Computer Science
  • Scalable Comput. Pract. Exp.
  • 2010
TLDR
This comprehensive test/reference provides a foundation for the understanding and implementation of parallel programming skills which are needed to achieve breakthrough results by developing parallel applications that perform well on certain classes of Graphic Processor Units (GPUs). Expand
GPU clusters for high-performance computing
TLDR
This paper presents efforts to address some of the challenges with building and running GPU clusters in HPC environments and touches upon such issues as balanced cluster architecture, resource sharing in a cluster environment, programming models, and applications for GPU clusters. Expand
Gaussian Processes for Machine Learning
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification. Expand
The OpenCL specification
  • A. Munshi
  • Computer Science
  • 2009 IEEE Hot Chips 21 Symposium (HCS)
  • 2009
TLDR
The specification is divided into a core specification that any OpenCL compliant implementation must support; a handheld/embedded profile which relaxes the OpenCL compliance requirements for handheld and embedded devices; and a set of optional extensions that are likely to move into the core specification in later revisions of the Opencl specification. Expand
...
1
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