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ieee International Geoscience and Remote Sensing symposium

作者： Jose M. Rodriguez Alves Jose M. P. Nascimento Jose M. Bioucas-Dias Vitor Silva Antonio Plaza Instituto de Telecomunicacoes Hyperspectral Computing Laboratory University of Extremadura

ISBN: (纸本)9781467311601

Vertex component analysis (VCA) has become a very popular and useful tool to linear unmix large hyperspectral datasets without the use of any a priori knowledge of the constituent spectra. Although VCA is fast method, many hyperspectral imagery applications require a response in real time or near-real time. This paper proposes two different optimizations for accelerating the computational performance of VCA: the first one focus a parallel implementation based on graphics computing units (GPUs) to alleviate the VCA computational burden;The second one is focused on the development of a strategy to remove a large proportion of mixed pixels that play no effect on the VCA functioning. Experiments are conducted using simulated and real hyperspectral datasets. These results reveal considerable acceleration factors, which satisfies the real-time constraints given by the data acquisition rate.

关键词： Hyperspectral Unmixing Endmember Extraction Vertex Component Analysis graphics Processing Unit parallel Methods Value Control Amplifier acquisition rate speedup factor Time constraints graphic images Development policy

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Subband coding for large-scale scientific simulation data using JPEG 2000

Subband coding for large-scale scientific simulation data us...

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ieee Southwest symposium on Image Analysis and Interpretation

作者： Christopher M. Brislawn Jonathan L. Woodring Susan M. Mniszewski David E. DeMarle James P. Ahrens Los Alamos National Laboratory Los Alamos NM USA Kitware Inc. Clifton Park NY USA

The ISO/IEC JPEG 2000 image coding standard is a family of source coding algorithms targeting high-resolution image communications. JPEG 2000 features highly scalable embedded coding features that allow one to interactively zoom out to reduced resolution thumbnails of enormous data sets or to zoom in on highly localized regions of interest with very economical communications and rendering requirements. While intended for fixed-precision input data, the implementation of the irreversible version of the standard is often done internally in floating point arithmetic. Moreover, the standard is designed to support high-bit-depth data. Part 2 of the standard also provides support for three-dimensional data sets such as multicomponent or volumetric imagery. These features make JPEG 2000 an appealing candidate for highly scalable communications coding and visualization of two- and three-dimensional data produced by scientific simulation software. We present results of initial experiments applying JPEG 2000 to scientific simulation data produced by the parallel Ocean Program (POP) global ocean circulation model, highlighting both the promise and the many challenges this approach holds for scientific visualization applications.

关键词： Transform coding Encoding data visualization Image coding data models Wavelet transforms

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Efficient Quality Threshold Clustering for parallel Architectures

Efficient Quality Threshold Clustering for Parallel Architec...

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Anthony Danalis Collin McCurdy Jeffrey S. Vetter University of Tennessee Knoxville TN USA Oak Ridge National Laboratory Oak Ridge TN USA Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781467309752

Quality Threshold Clustering (QTC) is an algorithm for partitioning data, in fields such as biology, where clustering of large data-sets can aid scientific discovery. Unlike other clustering algorithms, QTC does not require knowing the number of clusters a priori, however, its perceived need for high computing power often makes it an unattractive choice. This paper presents a thorough study of QTC. We analyze the worst case complexity of the algorithm and discuss methods to reduce it by trading memory for computation. We also demonstrate how the expected running time of QTC is affected by the structure of the input data. We describe how QTC can be parallelized, and discuss implementation details of our thread-parallel, GPU, and distributed memory implementations of the algorithm. We demonstrate the efficiency of our implementations through experimental data. We show how data sets with tens of thousands of elements can be clustered in a matter of minutes in a modern GPU, and seconds in a small scale cluster of multi-core CPUs, or multiple GPUs. Finally, we discuss how user selected parameters, as well as algorithmic and implementation choices, affect performance.

关键词： Complexity theory Clustering algorithms Proteins Algorithm design and analysis Memory management graphics processing unit Upper bound

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parallel Implementation and Performance Analysis of a 3D Oil Reservoir data visualization Tool on the Cell Broadband Engine and CUDA GPU

Parallel Implementation and Performance Analysis of a 3D Oil...

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ieee International Conference on High Performance Computing and Communications (HPCC)

作者： Fadi N. Siba Saadullah Mohammad Hashir Karim Kidwai Bibrak Qamar Falah Awwad Saudi Aramco R&D Center Dhahran Saudi Arabia IBM Cell Center of Competency and Faculty of Information Technology UAE University Al Ain United Arab Emirates

ISBN: (纸本)9781467321648

Usefulness of graphically visualizing and manipulating large data sets in oil and gas exploration and production is as important as ever. This paper describes the development and parallelization of a multi-phase 3D oil-water reservoir visualization tool on the IBM Cell computer and CUDA enabled GPU. An independent Oil reservoir simulator described in [1] was used to generate the pressure and oil / water saturation values over a certain period of time. The oil reservoir visualization tool displays data grids in a 3D environment and allows the user to interact with it. Due to large speed requirements, our aim is to parallelize the computations required to interact with and visualize the grid, mainly transformation [2], zooming, camera movement [3] and compute intensive lighting model [4][5]. This tool also allows the user to playback the simulation results over a time duration and fetches data values upon mouse click at a particular grid point on a particular day. The development environments are nVIDIA CUDA and IBM Cell SDK 3.0 along with QT and OpenGL libraries. Various experiments were run on an ×86 computer with nVIDIA Quadro FX 5800 GPU, and on an IBM Cell BE computer with 1 QS20 Cell blade containing two 9-core Cell processor packages. Our results indicate that the nVIDIA GPU provides on average, speed up of 67× over serial implementation and IBM Cell BE with 16 SPE SIMD implementation 32× over the serial implementation.

关键词： graphics processing unit Vectors Image color analysis Lighting data visualization Reservoirs Cameras

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Range Query Processing in a Multi-GPU Environment

Range Query Processing in a Multi-GPU Environment

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International symposium on parallel and Distributed Processing with Applications, ISPA

作者： Ricardo J. Barrientos José I. Gómez Christian Tenllado Manuel Prieto Matias Mauricio Marin Department of Computer Architecture ArTeCS Group Complutense University of Madrid Spain DIINF Yahoo Research Latin America University of Santiago Santiago Chile

Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the acceleration of on-line searches. However, to maintain an acceptable throughput, it is essential to exploit the intrinsic parallelism of the algorithms used for the on-line query solving process, even with indexed databases. Therefore, many strategies have been proposed in the literature to parallelize these algorithms, both on shared and distributed memory multiprocessor systems. Lately, GPUs have also been used to implement brute-force approaches instead of using indexing structures, due to the difficulties introduced by the index in the efficient exploitation of the GPU resources. In this work we propose a Multi-GPU metric-space technique that efficiently exploits index data structures for similarity search in large databases, and show how it outperforms previous OpenMP and GPU brute-force strategies. Furthermore, our analysis covers the effects of the database size and its nature.

关键词： graphics processing unit Indexes Instruction sets parallel processing Measurement Kernel

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Angular Histograms: Frequency-Based visualizations for large, High Dimensional data

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2011年第12期17卷 2572-2580页

作者： Geng, Zhao Peng, ZhenMin Laramee, Robert S. Walker, Rick Roberts, Jonathan C. Swansea Univ Visual Comp Grp Swansea W Glam Wales Bangor Univ Bangor Gwynedd Wales

parallel coordinates is a popular and well-known multivariate data visualization technique. However, one of their inherent limitations has to do with the rendering of very large data sets. This often causes an overplotting problem and the goal of the visual information seeking mantra is hampered because of a cluttered overview and non-interactive update rates. In this paper, we propose two novel solutions, namely, angular histograms and attribute curves. These techniques are frequency-based approaches to large, high-dimensional data visualization. They are able to convey both the density of underlying polylines and their slopes. Angular histogram and attribute curves offer an intuitive way for the user to explore the clustering, linear correlations and outliers in large data sets without the over-plotting and clutter problems associated with traditional parallel coordinates. We demonstrate the results on a wide variety of data sets including real-world, high-dimensional biological data. Finally, we compare our methods with the other popular frequency-based algorithms.

关键词： parallel Coordinates Angular Histogram Attribute Curves

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Sequence Alignment on Massively parallel Heterogeneous Systems

Sequence Alignment on Massively Parallel Heterogeneous Syste...

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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Aleksandr Drozd Naoya Maruyama Satoshi Matsuoka Graduate School of Information Science and Technology Tokyo Institute of Technology Meguro Tokyo Japan

ISBN: (纸本)9781467309745

Bioinformatics is a quickly emerging area of science with many important applications to human life. Sequence alignment in various forms is one of the main instruments used in bioinformatics. This work is motivated by the ever-increasing amount of sequence data that requires more and more computation power for its processing. This task calls for new GPU-based systems and their higher computational potential and energy efficiency as compared to CPUs. We address the problem of facilitating faster sequence alignment using modern multi-GPU clusters. Our initial step was to develop a fast and scalable GPU exact short sequence aligner. We used matching algorithm with small memory footprint based on Burrows-Wheeler transform. We developed a mathematical model of computation and communication costs to find optimal memory partitioning strategy for index and queries. Our solution achieves 10 times speedup over previous implementation based on suffix array on one GPU and scales to multiple GPUs. Our next step will be to adapt the suggested data structure and performance model for multi-node multi-GPU approximate sequence alignment. It is also planned to use exact matching to detect common regions in large sequences and use it as an intermediate step in full-scale genome comparison.

关键词： Indexes graphics processing unit Bioinformatics Genomics Performance evaluation Arrays Complexity theory

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Guest Editor's Introduction: Special Section on the Eurographics symposium on parallel graphics and visualization (EGPGV)

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ieee TRANSACTIONS ON visualization AND COMPUTER graphics 2011年第2期17卷 130-131页

作者： Comba, Joao Weiskopf, Daniel Univ Fed Rio Grande do Sul Inst Informat Graph Grp BR-91501970 Porto Alegre RS Brazil Univ Stuttgart Visualizat Res Ctr VISUS D-70569 Stuttgart Germany

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Performance Portability with the Chapel Language

Performance Portability with the Chapel Language

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Albert Sidelnik Saeed Maleki Bradford L. Chamberlain María J. Garzar'n David Padua Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois USA Cray Inc. Seattle Washington USA

It has been widely shown that high-throughput computing architectures such as GPUs offer large performance gains compared with their traditional low-latency counterparts for many applications. The downside to these architectures is that the current programming models present numerous challenges to the programmer: lower-level languages, loss of portability across different architectures, explicit data movement, and challenges in performance optimization. This paper presents novel methods and compiler transformations that increase programmer productivity by enabling users of the language Chapel to provide a single code implementation that the compiler can then use to target not only conventional multiprocessors, but also high-throughput and hybrid machines. Rather than resorting to different parallel libraries or annotations for a given parallel platform, this work leverages a language that has been designed from first principles to address the challenge of programming for parallelism and locality. This also has the advantage of providing portability across different parallel architectures. Finally, this work presents experimental results from the Parboil benchmark suite which demonstrate that codes written in Chapel achieve performance comparable to the original versions implemented in CUDA on both GPUs and multicore platforms.

关键词： graphics processing unit Arrays Multicore processing parallel processing Benchmark testing Reactive power

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Performance Study of SIMD Programming Models on Intel Multicore Processors

Performance Study of SIMD Programming Models on Intel Multic...

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ieee International symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Peter Kristof Hongtao Yu Zhiyuan Li Xinmin Tian Department of Computer Science Purdue University West Lafayette IN USA Mobile Computing and Compilers SSG Intel Corporation Santa Clara CA USA

ISBN: (纸本)9781467309745

Modern multicore hardware employs a variety of parallel execution units, including multiple CPU cores for executing multiple threads simultaneously, vector units such as the Intel SIMD on the CPU cores, as well as GPU-like processing arrays. Availability of such unprecedented level of parallelism on main-stream computers offers an enormous potential to enable a new generation of computation-intensive nontraditional applications. On the other hand, how to best harness the hardware parallelism presents a new challenge to application programmers, language designers and compiler developers. In this paper, we evaluate the impact of several different parallel execution models, especially the new SIMD vectorization methods, supported by the latest Intel ICC compiler (version 12.1), using three computation-intensive nontraditional parallel applications as the test workload. Unlike traditional numerical programs, these applications use highly irregular data structures and therefore present nontrivial challenges to effective use of SIMD vector units. The first application is a game engine architecture requiring real-time performance. The second application involves a kd-tree traversal, which is typical to the state-of-the-art 3D ray-tracing applications. The last application processes data for large-scale weather visualization system in the order of tens of minutes. We compare the execution time of these codes using different SIMD models supported by ICC in conjunction with parallel threading under TBB and OpenMP.

关键词： Vectors Games Program processors parallel processing Engines Benchmark testing Computational modeling

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