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16
On cognitive informatics
 Proceedings of ICCI’02
, 2002
"... We would like to thank Jim Basney at the NCSA for his technical support on the use of the GiB testbed. We are also grateful to Jun Ni and Boyd Knosp in the Research ..."
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Cited by 26 (5 self)
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We would like to thank Jim Basney at the NCSA for his technical support on the use of the GiB testbed. We are also grateful to Jun Ni and Boyd Knosp in the Research
Adaptive finite volume methods for distributed nonsmooth parameter identification
, 2007
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An OcTree Multigrid Method for QuasiStatic Maxwell’s Equations with Highly Discontinuous Coefficients
, 2006
"... In this paper we develop an OcTree discretization for Maxwell’s equations in the quasistatic regime. We then use this discretization in order to develop a multigrid method for Maxwell’s equations with highly discontinuous coefficients. We test our algorithms and compare it to other multilevel algor ..."
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Cited by 11 (2 self)
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In this paper we develop an OcTree discretization for Maxwell’s equations in the quasistatic regime. We then use this discretization in order to develop a multigrid method for Maxwell’s equations with highly discontinuous coefficients. We test our algorithms and compare it to other multilevel algorithms. 1
Location based placement of whole distributed systems
 In Proceedings of the 1st Conference on Emerging Network Experiments and Technologies
, 2005
"... The high bandwidth and low latency of the modern internet has made possible the deployment of distributed computing platforms. The XenoServer platform provides a distributed computing platform open to all and presents three major new challenges for resource discovery: Firstly, network location is ..."
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Cited by 10 (1 self)
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The high bandwidth and low latency of the modern internet has made possible the deployment of distributed computing platforms. The XenoServer platform provides a distributed computing platform open to all and presents three major new challenges for resource discovery: Firstly, network location is key for effectively provisioning services, to mitigate against highlatency, highload or component failure. Secondly, many services require a presence on several servers, with interrelated requirements. Finally, as the platform is open with respect to users and servers, large numbers of queries and updates are expected. To address these requirements we introduce and evaluate XenoSearch, a new distributed service for selecting the machines to host components of multinode distributed systems and which is uniquely able to express and efficiently answer complex queries with interrelated location constraints. We demonstrate that XenoSearch represents a tradeoff between accuracy and query time which avoids exhaustive search and supports multiple resources. In addition the performance of the algorithm and the quality of its server selections is investigated and the performance of the distributed service shown to be invariant as the number of nodes or items indexed increases.
From point cloud to grid DEM: A scalable approach
 In Proc. 12th International Symposium on Spatial Data Handling
, 2006
"... Summary. Given a set S of points in R 3 sampled from an elevation function H: R 2 → R, we present a scalable algorithm for constructing a grid digital elevation model (DEM). Our algorithm consists of three stages: First, we construct a quad tree on S to partition the point set into a set of nonover ..."
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Cited by 10 (6 self)
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Summary. Given a set S of points in R 3 sampled from an elevation function H: R 2 → R, we present a scalable algorithm for constructing a grid digital elevation model (DEM). Our algorithm consists of three stages: First, we construct a quad tree on S to partition the point set into a set of nonoverlapping segments. Next, for each segment q, we compute the set of points in q and all segments neighboring q. Finally, we interpolate each segment independently using points within the segment and its neighboring segments. Data sets acquired by LIDAR and other modern mapping technologies consist of hundreds of millions of points and are too large to fit in main memory. When processing such massive data sets, the transfer of data between disk and main memory (also called I/O), rather than the CPU time, becomes the performance bottleneck. We therefore present an I/Oefficient algorithm for constructing a grid DEM. Our experiments show that the algorithm scales to data sets much larger than the size of main memory, while existing algorithms do not scale. For example, using a machine with 1GB RAM, we were able to construct a grid DEM containing 1.3 billion cells (occupying 1.2GB) from a LIDAR data set of over 390 million points (occupying 20GB) in about 53 hours. Neither ArcGIS nor GRASS, two popular GIS products, were able to process this data set. 1
An OcTree Method for Parametric Image Registration
, 2006
"... Already for reasonable sized 3D images, image registration becomes a computationally intensive task. Here, we introduce and explore the concept of OcTree’s for registration which drastically reduces the number of processed data and thus the computational costs. We show how to map the registration pr ..."
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Cited by 4 (1 self)
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Already for reasonable sized 3D images, image registration becomes a computationally intensive task. Here, we introduce and explore the concept of OcTree’s for registration which drastically reduces the number of processed data and thus the computational costs. We show how to map the registration problem onto an OcTree and present a suitable optimization technique. Furthermore, we demonstrate the performance of the new approach by academic as well as real life examples. These examples indicate that the computational time can be reduced by a factor of 10 compared with standard approaches. 1
I/Oefficient algorithms and applications in geographic information systems
, 2007
"... Modern remote sensing methods such a laser altimetry (lidar) and Interferometric Synthetic Aperture Radar (IfSAR) produce georeferenced elevation data at unprecedented rates. Many Geographic Information System (GIS) algorithms designed for terrain modelling applications cannot process these massive ..."
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Cited by 3 (1 self)
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Modern remote sensing methods such a laser altimetry (lidar) and Interferometric Synthetic Aperture Radar (IfSAR) produce georeferenced elevation data at unprecedented rates. Many Geographic Information System (GIS) algorithms designed for terrain modelling applications cannot process these massive data sets. The primary problem is that these data sets are too large to fit in the main internal memory of modern computers and must therefore reside on larger, but considerably slower disks. In these applications, the transfer of data between disk and main memory, or I/O, becomes the primary bottleneck. Working in a theoretical model that more accurately represents this two level memory hierarchy, we can develop algorithms that are I/Oefficient and reduce the amount of disk I/O needed to solve a problem. In this thesis we aim to modernize GIS algorithms and develop a number of I/Oefficient algorithms for processing geographic data derived from massive elevation data sets. For each application, we convert a geographic question to an algorithmic question, develop an I/Oefficient algorithm that is theoretically efficient, implement our approach and verify its performance using realworld data. The applications we consider include constructing a gridded digital elevation model (DEM) from an
Hybrid MPI/GPU Interpolation for Grid DEM Construction
, 2012
"... The proliferation of lidar technology in remote sensing has resulted in extremely large, high resolution point clouds covering a wide variety of terrain. Constructing a grid digital elevation model (DEM) from these large data sets requires extensive computational resources and ample disk space. We p ..."
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The proliferation of lidar technology in remote sensing has resulted in extremely large, high resolution point clouds covering a wide variety of terrain. Constructing a grid digital elevation model (DEM) from these large data sets requires extensive computational resources and ample disk space. We propose a framework for leveraging modern computing resources including multicore distributed systems and general purpose GPU computing to reduce computational bottlenecks and accelerate DEM construction. We employ an I/Oefficient strategy using quad trees to automatically partition the lidar point clouds into a set of independent work bundles. We then distribute these work bundles to multiple GPUequipped hosts which independently interpolate a portion of the DEM and return partial results. Finally, we gather the partial results and assemble the final DEM I/Oefficiently. Our approach balances I/O, computation, and network communication to reduce bottlenecks. Experimental results show that our approach scales linearly with the number of compute hosts, and achieves speedups of 25 × or greater using GPU computing. These results make it practical to use more complex interpolation methods such as regularized splines with tension, which provide geomorphological advantages over simpler interpolation methods such as linear interpolation, nearest neighbor interpolation, or natural neighbor interpolation.
Registration
, 2006
"... Already for reasonable sized 3D images, image registration becomes a computationally intensive task. Here, we introduce and explore the concept of OcTree’s for registration which drastically reduces the number of processed data and thus the computational costs. We show how to map the registration pr ..."
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Already for reasonable sized 3D images, image registration becomes a computationally intensive task. Here, we introduce and explore the concept of OcTree’s for registration which drastically reduces the number of processed data and thus the computational costs. We show how to map the registration problem onto an OcTree and present a suitable optimization technique. Furthermore, we demonstrate the performance of the new approach by academic as well as real life examples. These examples indicate that the computational time can be reduced by a factor of 10 compared with standard approaches. 1
Maxwell’s Equations with Highly Discontinuous Coefficients
, 2006
"... In this paper we develop an OcTree discretization for Maxwell’s equations in the quasistatic regime. We then use this discretization in order to develop a multigrid method for Maxwell’s equations with highly discontinuous coefficients. We test our algorithms and compare it to other multilevel algor ..."
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In this paper we develop an OcTree discretization for Maxwell’s equations in the quasistatic regime. We then use this discretization in order to develop a multigrid method for Maxwell’s equations with highly discontinuous coefficients. We test our algorithms and compare it to other multilevel algorithms. 1