Geographically Weighted Regression (GWR) is a local version of spatial regression that captures spatial dependency in regression analysis. GWR has many application in practice as a visualization and prediction tool fo...
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ISBN:
(纸本)9781467389297
Geographically Weighted Regression (GWR) is a local version of spatial regression that captures spatial dependency in regression analysis. GWR has many application in practice as a visualization and prediction tool for spatial exploration (e.g in climate, economy, medical). However, this locally regression model is slow in process upon the volume of calculations and the spatial getting bigger. Improving performance of GWR is a critical issue, but their distributed implementations have not been studied. Recently, with the advent of Spark as well MapReduce framework, the development of machine learning applications and parallel programming becomes easier. In this article, we propose several large-scale implementations of distributed GWR, leveraging Spark framework. We implemented and evaluated these approaches with large datasets. To our best knowledge, this is the first work addressing GWR at large-scale.
NASA Technical Reports Server (Ntrs) 20030032283: Using Grid Benchmarks for Dynamic Scheduling of Grid Applications by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 20030032283: Using Grid Benchmarks for Dynamic Scheduling of Grid Applications by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19770015903: parallel Compilation: a Design and Its Application to Simula 67 by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19770015903: parallel Compilation: a Design and Its Application to Simula 67 by NASA Technical Reports Server (Ntrs); published by
We propose an unified parallel programming framework which supports both heterogeneity and fault tolerance in MPI programs on a variety of parallel computing platforms. This paper is mainly dedicated to heterogeneity ...
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ISBN:
(纸本)9781424468904
We propose an unified parallel programming framework which supports both heterogeneity and fault tolerance in MPI programs on a variety of parallel computing platforms. This paper is mainly dedicated to heterogeneity support in our framework. In our framework, a variety of parallel and sequential jobs submitted by multiple users are optimally scheduled on heterogeneous parallel computing environment. To balance the loads among the nodes on such heterogeneous computing environments, some of the parallel processes should be transferred between the nodes. We adopted the migration facility provided by Xen virtualization to realize a load balancing system where an MPI process running on a Xen virtual machine is migrated between the nodes. We confirmed that the protype system offers efficient load balancing facilities for heterogeneous computing environment with low overhead incurred by Xen virtualization.
A scale-out system is a cluster of commodity machines, and offers a good platform to support steadily increasing workloads that process growing data sets. Sharding [4] is a method of partitioning data and processing a...
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ISBN:
(纸本)9781605589084
A scale-out system is a cluster of commodity machines, and offers a good platform to support steadily increasing workloads that process growing data sets. Sharding [4] is a method of partitioning data and processing a computation on a scale-out system. In a database system, a large table can be partitioned into small tables so each node can process its part of the computation. The sharding approach in a large batch transaction processing, which is important in financial area, presents two hard problems to programmers. Programmers have to write complex code (1) to transfer the input data so as to align the computations with the data partitions, and (2) to manage the distributed transactions. This paper presents a new parallel programming framework that makes parallel transactional programming easier by specifying transaction scopes and partitioners to simplify the code. Transaction scopes include series of subtransactions, each of which performs local operations. The system manages the distributed transactions automatically. A partitioner represents how the computation should be decomposed and aligned with the data partitions to avoid remote database accesses. Between paired of subtransactions, the system handles the data shuffling across the network. We implemented our parallel programming framework as a new Java class library. We hide all of the complex details of data transfer and distributed transaction management in the library. Our programming framework can eliminate almost 66% of the lines of code compared to a current programming approach without programming framework support. We also confirmed good scalability, with a scaling factor of 20.6 on 24 nodes using our modified batch program for the TPC-C benchmark. Copyright 2010 ACM.
The images generated during radiation oncology treatments provide a valuable resource to conduct analysis for personalized therapy, outcomes prediction, and treatment margin optimization. Deformable image registration...
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The images generated during radiation oncology treatments provide a valuable resource to conduct analysis for personalized therapy, outcomes prediction, and treatment margin optimization. Deformable image registration (DIR) is an essential tool in analyzing these images. We are enhancing and examining DIR with the contributions of this paper: 1) implementing and investigating a cloud and graphic processing unit (GPU) accelerated DIR solution and 2) assessing the accuracy and flexibility of that solution on planning computed tomography (CT) with cone-beam CT (CBCT). Registering planning CTs and CBCTs aids in monitoring tumors, tracking body changes, and assuring that the treatment is executed as planned. This provides significant information not only on the level of a single patient, but also for an oncology department. However, traditional methods for DIR are usually time-consuming, and manual intervention is sometimes required even for a single registration. In this paper, we present a cloud-based solution in order to increase the data analysis throughput, so that treatment tracking results may be delivered at the time of care. We assess our solution in terms of accuracy and flexibility compared with a commercial tool registering CT with CBCT. The latency of a previously reported mutual information-based DIR algorithm was improved with GPUs for a single registration. This registration consists of rigid registration followed by volume subdivision-based nonrigid registration. In this paper, the throughput of the system was accelerated on the cloud for hundreds of data analysis pairs. Nine clinical cases of head and neck cancer patients were utilized to quantitatively evaluate the accuracy and throughput. Target registration error (TRE) and structural similarity index were utilized as evaluation metrics for registration accuracy. The total computation time consisting of preprocessing the data, running the registration, and analyzing the results was used to evaluate th
The classical solution of electromagnetic problems using the finite element (FE) method needs to assemble, store and solve an Ax = b matrix system. A new technique for solving FE cases, considered much simpler than tr...
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We show that a careful parallelization of statistical multiresolution estimation (SMRE) improves the phase reconstruction in X-ray near-field holography. The central step in, and the computationally most expensive par...
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We show that a careful parallelization of statistical multiresolution estimation (SMRE) improves the phase reconstruction in X-ray near-field holography. The central step in, and the computationally most expensive part of, SMRE methods is Dykstra's algorithm. It projects a given vector onto the intersection of convex sets. We discuss its implementation on NVIDIA's compute unified device architecture (CUDA). Compared to a CPU implementation parallelized with OpenMP, our CUDA implementation is up to one order of magnitude faster. Our results show that a careful parallelization of Dykstra's algorithm enables its use in large-scale statistical multiresolution analyses.
Leaks in water distribution systems waste energy and water resources, increase damage to infrastructure, and may allow contamination of potable water. This research develops an evolutionary algorithm-based approach to...
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Leaks in water distribution systems waste energy and water resources, increase damage to infrastructure, and may allow contamination of potable water. This research develops an evolutionary algorithm-based approach to minimize the cost of water loss, new infrastructure, and operations that reduce background leakage. A new design approach is introduced that minimizes capital and operational costs, including energy and water loss costs. Design decisions identify a combination of infrastructure improvements, including pipe replacement and valve installment, and operation rules for tanks and pumps. Solution approaches are developed to solve both a single-objective and multiobjective problem formulation. A genetic algorithm and a nondominated sorting genetic algorithm are implemented within a high-performance computing platform to select tank sizes, pump placement and operations, placement of pressure-reducing valves, and pipe diameters for replacing pipes. The evolutionary algorithm approaches identify solutions that minimize water loss due to leakage, operational costs, and capital costs, while maintaining pressure at nodes and operational feasibility for tanks and pumps. Solutions are compared to identify a recommended design. The framework is demonstrated to redesign a water distribution system for an illustrative case study, C-Town. (C) 2015 American Society of Civil Engineers.
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