The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hypersp...
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ISBN:
(纸本)9781467372220
The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hyperspectral information processing. To improve the accuracy of hyperspectral image classification, we propose a classification method based on the spatial-spectral join-t contextual sparse coding. Firstly, a dictionary is obtained by training using samples selected from the ground-truth reference data. Then, the sparse coefficients of each pixel are calculated based on the learned dictionary. Afterward, the sparse coefficients are input to the classifier and the final classification result is obtained. The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing is used to evaluate the performance of the proposed approach. Experimental results show that the proposed method yields the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 in comparison with other classification methods.
Message Passing Interfaces (MPI) plays an important role in parallel computing. Many parallel applications are implemented as MPI programs. The existing methods of bug detection for MPI programs have the shortage of p...
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ISBN:
(纸本)9781479981120
Message Passing Interfaces (MPI) plays an important role in parallel computing. Many parallel applications are implemented as MPI programs. The existing methods of bug detection for MPI programs have the shortage of providing both input and non-determinism coverage, leading to missed bugs. In this paper, we employ symbolic execution to ensure the input coverage, and propose an on-the-fly schedule algorithm to reduce the interleaving explorations for non-determinism coverage, while ensuring the soundness and completeness. We have implemented our approach as a tool, called MPISE, which can automatically detect the deadlock and runtime bugs in MPI programs. The results of the experiments on benchmark programs and real world MPI programs indicate that MPISE finds bugs effectively and efficiently. In addition, our tool also provides diagnostic information and replay mechanism to help understand bugs.
In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers ...
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In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers to mapping a large number of workloads provided by cloud users/tenants to substrate network provided by cloud providers. Although the existing heuristic approaches are able to find a feasible solution, the quality of the solution is not guaranteed. Concerning this issue, based on the minimum mapping cost, this paper solves the resource allocation problem by modeling it as a distributed constraint optimization problem. Then an efficient approach is proposed to solve the resource allocation problem, aiming to find a feasible solution and ensuring the optimality of the solution. Finally, theoretical analysis and extensive experiments have demonstrated the effectiveness and efficiency of our proposed approach.
The computation core of many big data applications can be expressed as general matrix computations, including linear algebra operations and irregular matrix operations. However, existing parallel programming systems s...
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The computation core of many big data applications can be expressed as general matrix computations, including linear algebra operations and irregular matrix operations. However, existing parallel programming systems such as Spark do not have programming abstraction and efficient implementation for general matrix computations. In this paper, we present MatrixMap, a unified and efficient data-parallel system for general matrix computations. MatrixMap provides powerful yet simple abstraction, consisting of a distributed data structure called bulk key matrix and a computation interface defined by matrix patterns. Users can easily load data into bulk key matrices and program algorithms into parallel matrix patterns. MatrixMap outperforms current state-of-the-art systems by employing three key techniques: matrix patterns with lambda functions for irregular and linear algebra matrix operations, asynchronous computation pipeline with optimized data shuffling strategies for specific matrix patterns and in-memory data structure reusing data in iterations. Moreover, it can automatically handle the parallelization and distribute execution of programs on a large cluster. The experiment results show that MatrixMap is 12 times faster than Spark.
The publish/subscribe(pub/sub)paradigm is a popular communication model for data dissemination in large-scale distributed ***,scalability comes with a contradiction between the delivery latency and the memory *** one ...
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The publish/subscribe(pub/sub)paradigm is a popular communication model for data dissemination in large-scale distributed ***,scalability comes with a contradiction between the delivery latency and the memory *** one hand,constructing a separate overly per topic guarantees real-time dissemination,while the number of node degrees rapidly increases with the number of *** the other hand,maintaining a bounded number of connections per node guarantees small memory cost,while each message has to traverse a large number of uninterested nodes before reaching the *** this paper,we propose Feverfew,a coverage-based hybrid overlay that disseminates messages to all subscribers without uninterested nodes involved in,and increases the average number of node connections slowly with an increase in the number of subscribers and *** major novelty of Feverfew lies in its heuristic coverage mechanism implemented by combining a gossip-based sampling protocol with a probabilistic searching *** on the practical workload,our experimental results show that Feverfew significantly outperforms existing coverage-based overlay and DHT-based overlay in various dynamic network environments.
The volume of malwares is growing at an exponential speed nowadays. This huge growth makes it extremely hard to analyse malware manually. Most existing signatures extracting methods are based on string signatures, and...
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Power consumption, design complexity and areacost are limiting constraints in the design of interconnect for scalable many-core systems. To tackle the power and area concerns, we propose a light-weight unidirectional ...
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ISBN:
(纸本)9781479974375
Power consumption, design complexity and areacost are limiting constraints in the design of interconnect for scalable many-core systems. To tackle the power and area concerns, we propose a light-weight unidirectional channel network-on-chip in 2D mesh topology (UniMESH), which simplifies router architectures, uses only half amount of channel links to guarantee a fully connected topology, and adopts a novel routing algorithm and deadlock recovery mechanism. As a result, it can reduce both design complexity and area-cost, and decrease some unwanted power consumption. Evaluations show that the proposed light-weight UniMESH can reduce 57.4% router areas, and save 39.3% total power consumption and only add few extra latency when compared with conventional 2D mesh design in SPLASH application simulations.
In recent years, many companies are embracing the Hadoop MapReduce system for large-data processing with completion time constrains. However, exiting Hadoop schedulers still suffer from the reducer load imbalancing pr...
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ISBN:
(纸本)9781467381741
In recent years, many companies are embracing the Hadoop MapReduce system for large-data processing with completion time constrains. However, exiting Hadoop schedulers still suffer from the reducer load imbalancing problem. In this paper, we present a novel run-time load balancing method for MapReduce. Our approach predicts the workload of each reduce task at run-time, and assigns the reduce tasks to specified machines based on the estimated workload of reduce tasks dynamically. Therefore, our approach can achieve load balance among machines. The experimental results show that our approach achieves high accuracy while predicting the workload of reduce tasks, and improves the job completion time by up to 23.15%.
On June 17, 2013, MilkyWay-2 （Tianhe-2） supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design...
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On June 17, 2013, MilkyWay-2 （Tianhe-2） supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design of hardware and software systems. The key architecture features of MilkyWay-2 are highlighted, including neo-heterogeneous compute nodes integrating commodity- off-the-shelf processors and accelerators that share similar instruction set architecture, powerful networks that employ proprietary interconnection chips to support the massively parallel message-passing communications, proprietary 16- core processor designed for scientific computing, efficient software stacks that provide high performance file system, emerging programming model for heterogeneous systems, and intelligent system administration. We perform extensive evaluation with wide-ranging applications from LINPACK and Graph500 benchmarks to massively parallel software deployed in the system.
The fast numerical solutions of Riesz fractional equation have computational cost of O(NMlogM), where M, N are the number of grid points and time steps. In this paper, we present a GPU-based fast solution for Riesz sp...
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The fast numerical solutions of Riesz fractional equation have computational cost of O(NMlogM), where M, N are the number of grid points and time steps. In this paper, we present a GPU-based fast solution for Riesz space fractional equation. The GPU-based fast solution, which is based on the fast method using FFT and implemented with CUDA programming model, consists of parallel FFT, vector-vector addition and vector-vector multiplication on GPU. The experimental results show that the GPU-based fast solution compares well with the exact solution. Compared to the known parallel fast solution on 8-core Intel E5-2670 CPU, the overall performance speedup on NVIDIA GTX650 GPU reaches 2.12 times and that on NVIDIA K20C GPU achieves 10.93 times.
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