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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

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Numerical Simulation of Long-Term Fate of CO₂ Stored in Deep Reservoir Rocks on Massively parallel Vector Supercomputer 1

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10th international conference on High Performance Computing for Computational Science (VECPAR)

作者： Yamamoto, Hajime Nanai, Shinichi Zhang, Keni Audigane, Pascal Chiaberge, Christophe Ogata, Ryusei Nishikawa, Noriaki Hirokawa, Yuichi Shingu, Satoru Nakajima, Kengo Taisei Corp Yokohama Kanagawa Japan Beijing Normal Univ Beijing Peoples R China Bur Rech Geol & Minieres Orleans France NEC Corp Ltd Tokyo Japan Japan Agcy Marine Earth Sci & Technol Yokohama Kanagawa Japan Univ Tokyo Tokyo Japan

ISBN: (数字)9783642387180

ISBN: (纸本)9783642387180;9783642387173

As one of the promising approaches for reducing greenhouse-gas content in the atmosphere, CCS (carbon dioxide capture and storage) has been recognized worldwide. CO2 is captured from large emission sources and injected and stored in deep reservoir rocks, including saline aquifers, depleted oil and gas field. Under typical pressure and temperature conditions at deep reservoirs (depths > 800m), CO2 will be stored in supercritical state, subsequently dissolving in groundwater, and eventually forming carbonate minerals through geochemical reactions in a long-term (e. g., thousands of years). To ensure the safety and permanence of the storage, numerical simulation is considered as the most powerful approach for predicting the long-term fate of CO2 in reservoirs. A parallelized general-purpose hydrodynamics code TOUGH2-MP has been used on scalar architectures where it exhibits excellent performance and scalability. However, on the Earth Simulator (ES2), which is a massively parallel vector computer, extensive tune-ups were required for increasing the vector operation ratio. In this paper, the performance of the modified TOUGH2-MP code on ES2 is presented with some illustrative numerical simulations of long-term fate of CO2 stored in reservoirs.

关键词： CCS Hydrodynamics Vector processors the Earth Simulator

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When to reach for the cloud: Using parallel hardware for link discovery

When to reach for the cloud: Using parallel hardware for lin...

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10th international conference on the Semantic Web: Semantics and Big Data, ESWC 2013

作者： Ngomo, Axel-Cyrille Ngonga Kolb, Lars Heino, Norman Hartung, Michael Auer, Sören Rahm, Erhard Department of Computer Science University of Leipzig 04109 Leipzig Germany

ISBN: (纸本)9783642382871

With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. this paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： MapReduce

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parallel Foreground Object Detection on A Polymorphous Computer

Parallel Foreground Object Detection on A Polymorphous Compu...

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international Workshop on Intelligent Communication and Social Networks

作者： Yi Zhang Tao Li Jungang Han School of Computer Science Xi'An University of Posts and Telecom Shaanxi Research Center for Telecom ASIC Design Xi'An University of Posts and Telecom

ISBN: (纸本)9781479909735

Foreground object detection is an important area in computer vision research. It aims to detect, classify, and recognize object in video images. Real time video processing needs to deal with large amount of data. It is also computationally intensive. this paper presents the parallelization of foreground object detection algorithms on a polymorphous computer that is capable of data parallel, thread parallel and instruction parallel computation. A modified approach using background differentiation and image integration is adopted in this research and a parallel implementation is realized on the PAAG polymorphous array processor. the parallel implementation is able to perform effective real-time foreground object detection in fast video frames. Simulation results show that this approach is highly effective.

关键词： Foreground object detection Computer vision parallel computation Polymorphous processor Data parallelism thread level parallelism Instruction level parallelism

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Design space explorations for streaming accelerators using Streaming Architectural Simulator

Design space explorations for streaming accelerators using S...

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international Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Muhammad Shafiq Miquel Pericàs Nacho Navarro Eduard Ayguadé Centre of Excellence in Science and Applied Technologies (CESAT) Islamabad Pakistan JST CREST Tokyo Institute of Technology Tokyo Japan Computer Sciences Barcelona Supercomputing Center Barcelona Spain

In the recent years streaming accelerators like GPUs have been pop-up as an effective step towards parallel computing. the wish-list for these devices span from having a support for thousands of small cores to a nature very close to the general purpose computing. this makes the design space very vast for the future accelerators containing thousands of parallel streaming cores. this complicates to exercise a right choice of the architectural configuration for the next generation devices. However, accurate design space exploration tools developed for the massively parallel architectures can ease this task. the main objectives of this work are twofold. (i) We present a complete environment of a trace driven simulator named SArcs (Streaming Architectural Simulator) for the streaming accelerators. (ii) We use our simulation tool-chain for the design space explorations of the GPU like streaming architectures. Our design space explorations for different architectural aspects of a GPU like device a e with reference to a base line established for NVIDIA's Fermi architecture (GPU Tesla C2050). the explored aspects include the performation effects by the variations in the configurations of Streaming Multiprocessors Global Memory Bandwidth, Channles between SMs down to Memory Hierarchy and Cache Hierarchy. the explorations are performed using application kernels from Vector Reduction, 2D-Convolution. Matrix-Matrix Multiplication and 3D-Stencil. Results show that the configurations of the computational resources for the current Fermi GPU device can deliver higher performance with further improvement in the global memory bandwidth for the same device.

关键词： Graphics processing units Optical character recognition software Instruction sets Space exploration

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parallel implementations of motion estimation algorithms using OpenCL

Parallel implementations of motion estimation algorithms usi...

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international conference on Digital Signal processing (DSP)

作者： Antti Heikkinen Lance Fono VTT Technical Research Centre of Finland Oulu Finland

parallel processors such as Graphics processing units (GPUs) have emerged as co-processing units for central processing units (CPUs) to accelerate different applications. Open Computing Language (OpenCL) is a framework for multiprocessing in heterogeneous platforms. In this paper we focus on motion estimation which is the most time consuming task in video coding. We study two motion estimation algorithms in terms of parallel execution. We implemented the full search algorithm and the hierarchical search algorithm with OpenCL and with C code. Our measurements show that the OpenCL-based implementations of the algorithms on the GPU can achieve nearly 10 times speedup compared to the corresponding C implementation on a single CPU.

关键词： Motion estimation Graphics processing units Kernel Video coding Streaming media Signal processing algorithms Algorithm design and analysis

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Ray-Tracing-Based Geospatial Optimization for Heterogeneous architectures Enhancing Situational Awareness

Ray-Tracing-Based Geospatial Optimization for Heterogeneous ...

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IEEE international conference on Computational Science and Engineering, CSE

作者： David A. Richie James A. Ross Song J. Park Dale R. Shires Brown Deer Technology Forest Hill MD Dynamics Research Corp. Andover MA APG U.S. Army Research Laboratory MD

this paper presents the implementation of ray tracing-based algorithms for multi-objective geospatial optimization targeting various many-core processing technologies such as graphics processing units, x86 multi-cores, and ARM processors. High performance is achieved through highly parallel core algorithms, executed on multiple compute devices across a heterogeneous architecture using low-level OpenCL kernels. algorithms for calculating line-of-sight ballistic threat, visual observability, ground plane extraction, and Markov chain Monte Carlo optimization provide an augmented geospatial intelligence and situational awareness in three-dimensional urban environments.

关键词： Optimization Ray tracing Surveillance Computer architecture Graphics processing units Kernel Observability

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Conflict-free data access for multi-bank memory architectures using padding

Conflict-free data access for multi-bank memory architecture...

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international conference on High Performance Computing

作者： Joar Sohl Jian Wang Andréas Karlsson Dake Liu Department of Computer Engineering Linköping University Linköping Sweden

For high performance computation memory access is a major issue. Whether it is a supercomputer, a GPGPU device, or an Application Specific Instruction set Processor (ASIP) for Digital Signal processing (DSP) parallel execution is a necessity. A high rate of computation puts pressure on the memory access, and it is often non-trivial to maximize the data rate to the execution units. Many algorithms that from a computational point of view can be implemented efficiently on parallel architectures fail to achieve significant speed-ups. the reason is very often that the speed-up possible with the available execution units are poorly utilized due to inefficient data access. this paper shows a method for improving the access time for sequences of data that are completely static at the cost of extra memory. this is done by resolving memory conflicts by using padding. the method can be automatically applied and it is shown to significantly reduce the data access time for sorting and FFTs. the execution time for the FFT is improved with up to a factor of 3.4 and for sorting by a factor of up to 8.

关键词： Vectors Sorting Zinc Digital signal processing Registers Memory management

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GPU-Accelerated parallel 3D Image thinning

GPU-Accelerated Parallel 3D Image Thinning

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Bingfeng Hu Xuan Yang National High Performance Computing Center at Shenzhen Guangdong China

the skeletons of the objects in 3D images can be extracted by using 3D image thinning. the application of 3D image thinning for image analysis is hampered by its considerable computation time. By employing the graphics processing unit (GPU), which has tremendous powerful computing power at an incomparable performance-to-cost ratio, the calculation of 3D image thinning can be accelerated. In this paper, we proposed a parallel implementation approach on GPU for the 3D 12-subiteration image thinning algorithm, in which object voxels of 3D image are assigned to threads based on the characteristic of sparse 3D image data. the performance of our approach is analyzed with different image sizes, the ratio of object voxels and the number of thread grids on GPU. the performance of the traditional threads assignment strategy and new threads assignment strategy are compared to show that the proposed approach is more efficient.

关键词： Instruction sets three-dimensional displays Graphics processing units Kernel Shape parallel processing Arrays

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A parallel Depth-Aided Exemplar-Based Inpainting for Real-Time View Synthesis on GPU

A Parallel Depth-Aided Exemplar-Based Inpainting for Real-Ti...

引用

IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Zheng Tian Cheng Xu Xiaoyun Deng College of Information Science and Engineering Hunan University Changsha China R&D STMicroelectronics Asia Pacific Pte Ltd Singapore

ISBN: (纸本)9781479909735

Synthesizing new images from given image pair and their corresponding depth maps is an essential function for many 3D video applications. Exemplar-based in painting methods have been proposed in recent years to be used to restore newly synthesized images by strategically filling the missing pixels which don't have any references due to occlusion. Due to the prioritized filling process, the in painting methods usually result in high computational complexity and can hardly reach real-time performance. In this paper, a parallel depth-aided in painting method is proposed to address the efficiency issue of this kind of high performance algorithms. In order to reduce the computation, the proposed method searches for background pixels in a restricted search range on the reference images for effective context filling. then a partially parallel strategy is proposed to speedup the in painting process while maintaining its high restoration accuracy. Finally the method is implemented with CUDA on NVidia graphic card GTS450. the experiment results showed that the proposed method could produce the best on par results and is suitable for real-time multi-view image synthesis.

关键词： Filling Graphics processing units Equations Mathematical model Real-time systems Interpolation Cameras

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Efficient homology computations on multicore and manycore systems

Efficient homology computations on multicore and manycore sy...

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international conference on High Performance Computing

作者： N. Anurag Murty Vijay Natarajan Sathish Vadhiyar Supercomputer Education and Research Centre

Homology computations form an important step in topological data analysis that helps to identify connected components, holes, and voids in multi-dimensional data. Our work focuses on algorithms for homology computations of large simplicial complexes on multicore machines and on GPUs. this paper presents two parallel algorithms to compute homology. A core component of both algorithms is the algebraic reduction of a cell with respect to one of its faces while preserving the homology of the original simplicial complex. the first algorithm is a parallel version of an existing sequential implementation using OpenMP. the algorithm processes and reduces cells within each partition of the complex in parallel while minimizing sequential reductions on the partition boundaries. Cache misses are reduced by ensuring data locality for data in the same partition. We observe a linear speedup on algebraic reductions and an overall speedup of up to 4.9× with 16 cores over sequential reductions. the second algorithm is based on a novel approach for homology computations on manycore/GPU architectures. this GPU algorithm is memory efficient and capable of extremely fast computation of homology for simplicial complexes with millions of simplices. We observe up to 40× speedup in runtime over sequential reductions and up to 4.5× speedup over REDHOM library, which includes the sequential algebraic reductions together with other advanced homology engines supported in the software.

关键词： Multicore processing Partitioning algorithms Microprocessors Graphics processing units Instruction sets Software algorithms

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