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IEEE International symposium on Parallel and Distributed processing Workshops and Phd Forum (IPDPSW)

作者： Pingfan Li Xuhao Chen Zhe Quan Jianbin Fang Huayou Su Tao Tang Canqun Yang College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781509036837

Graph coloring has been broadly used to discover concurrency in parallel computing, where vertices with the same color represent subtasks that can be processed simultaneously. To speedup graph coloring for large scale datasets, parallel algorithms have been proposed to leverage the massive hardware resources on modern multicore CPUs or GPGPUs. Existing GPU implementations either have limited performance or yield unsatisfactory coloring quality (too many colors assigned). We present a high performance parallel graph coloring implementation on GPGPUs with good coloring quality. Our approach employs the speculative greedy algorithm which usually yields better quality than the method based on maximal independent set. In order to achieve high performance on GPGPUs, we adapt the algorithm to improve work efficiency and reduce overhead, and incorporate several optimization techniques which reduce memory access latency and atomic operation overhead. Our method is evaluated with both synthetic and real-world graphs on the NVIDIA GPU. Experimental results show that our proposed implementations outperform the sequential implementation (3.0× speedup) and the existing GPU implementation from the NVIDIA CUSPARSE library (1.5× speedup), while yielding good coloring quality close to the sequential implementation.

关键词： image color analysis graphics processing units Color Arrays Algorithm design and analysis Concurrent computing Parallel processing

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Automated control module based on VBM for shipyard welding applications: Study case on the Bug-O Matic Weaver Robot 12

Automated control module based on VBM for shipyard welding a...

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12th Latin American Robotics symposium / 3rd brazilian symposium on Robotics (LARS-SBR)

作者： Steffens, Cristiano Rafael Leonardo, Bruno Quaresma da Silva Filho, Sidnei Carlos Huttner, Valquiria Aguiar, Marcio Rozante Leivas, Eduardo do Amaral da Rosa, Vagner Santos da Costa Botelho, Silvia Silva Fed Univ Rio Grande Computat Sci Ctr Rio Grande RS Brazil

ISBN: (纸本)9781467371292

In shipbuilding, welding is a key process. The present paper proposes a VBM (Vision-Based Measurement) system for the automation of intelligent robotic welding applications. The proposed system is based on a single CMOS camera as visual sensor, for the image acquisition, and a FPGA (Field Programmable Gate Array) digital system, able to read the robot signals and adjust the weld pattern and welder equipment parameters in real time. The image processing and groove parameters extraction is performed using a standard computer. A full system prototype using a linear welding robot is presented. The evaluation of the VBM as a measuring equipment shows the approach capabilities.

关键词： automation computer vision image processing robot vbm welding

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Initial Study of Reconfigurable Neural Network Accelerators

Initial Study of Reconfigurable Neural Network Accelerators

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International symposium on Computing and Networking (CANDAR)

作者： Momoka Ohba Satoshi Shindo Shinobu Miwa Tomoaki Tsumura Hayato Yamaki Hiroki Honda University of Electron and Communications Tokyo Japan Nagoya Institute of Technology Aichi Japan

ISBN: (纸本)9781509026562

Neural networks or NNs are widely used for many machine learning applications such as image processing and speech recognition. Since general-purpose processors such as CPUs and GPUs are energy inefficient for computing NNs, application-specific hardware accelerators for NNs (a.k.a. neural network accelerators or NNAs) have been proposed to improve the energy efficiency. However, existing NNAs are too customized for computing specific NNs, and do not allow to change NN models or learning algorithms. This limitation prevents machine-learning researchers from exploiting NNAs, so we are developing a general-purpose NNA that has the capability to compute any NN. Our NNA equips with reconfigurable logic in addition to various custom logics, which is called reconfigurable NNA or RNNA. RNNA is highly tuned for the NN computation but allows end users to customize the hardware to compute their desired NN. This paper introduces the RNNA architecture, and reports the performance analysis of RNNA with a cycle-level simulator.

关键词： Artificial neural networks Neurons computer architecture Biological neural networks graphics processing units Computational modeling

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Efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems

Efficient convolutional neural networks for pixelwise classi...

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IEEE International symposium on Biomedical Imaging

作者： Fabian Tschopp Julien N. P. Martel Srinivas C. Turaga Matthew Cook Jan Funke Department of Computer Science Zurich Universitat Zurich Zurich ZH CH Janelia Research Campus Howard Hughes Medical Institute Institute of Neuroinformatics University of Zurich and ETH Zurich

ISBN: (纸本)9781479923519

With recent advances in high-throughput Electron Microscopy (EM) imaging it is now possible to image an entire nervous system of organisms like Drosophila melanogaster. One of the bottlenecks to reconstruct a connectome from these large volumes (≈ 100 TiB) is the pixel-wise prediction of membranes. The time it would typically take to process such a volume using a convolutional neural network (CNN) with a sliding window approach is in the order of years on a current GPU. With sliding windows, however, a lot of redundant computations are carried out. In this paper, we present an extension to the Caffe library to increase throughput by predicting many pixels at once. On a sliding window network successfully used for membrane classification, we show that our method achieves a speedup of up to 57×, maintaining identical prediction results.

关键词： Kernel Training Hardware Biological neural networks Libraries graphics processing units Throughput

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Parallel Decompression of Seismic Data on GPU Using a Lifting Wavelet Algorithm 20

Parallel Decompression of Seismic Data on GPU Using a Liftin...

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20th symposium ON SIGNAL processing, imageS AND computer VISION (STSIVA)

作者： Castelar, Jairo A. Angulo, Carlos A. Fajardo, Carlos A. Univ Ind Santander Bucaramanga Bucaramanga Santander Colombia

ISBN: (纸本)9781467394611

Subsurface images are widely used by the oil companies to find oil reservoirs. The construction of these images involves to collect and process a huge amount of seismic data. Generally, the oil companies use compression algorithms to reduce the storage and transmission costs. Currently, the compression process is developed on-site using CPU architectures, whereas the construction of the subsurface images is developed on GPU clusters. For this reason, the decompression process has to be developed on GPU architectures. So, fast and parallel decompression algorithms are required to be implemented on GPUs. We implemented an algorithm that performs the decompression of seismic traces on GPU. The algorithm is based on a 2D Lifting Wavelet Transform. The decompression algorithm was developed in CUDA 6.5 and implemented into a GeForce GTX660 GPU. This algorithm was tested using different data sets supplied by an oil company. Experimental results allowed us to establish how the compression ratio affects the performance of our algorithm. Additionally, we also show how the number of threads per block affects this performance.

关键词： data compression geophysical image processing graphics processing units hydrocarbon reservoirs image coding parallel architectures seismology wavelet transforms 2D lifting wavelet transform CUDA 6.5 GeForce GTX660 GPU compression algorithm oil companies oil reservoir seismic data parallel decompression subsurface image transmission cost reduction Algorithm design and analysis Discrete wavelet transforms Encoding graphics processing units image coding Quantization (signal) graphics processing Unit image coding Compression algorithms Oil industry Algorithm design and analysis discrete wavelet transform hydrocarbon reservoirs Parallel architectures seismology Data compression wavelet transforms Decompression GRAPPER PICK UP OIL RESERVOIR seismic data

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A Robust Methodology for Performance Analysis on Hybrid Embedded Multicore Architectures

A Robust Methodology for Performance Analysis on Hybrid Embe...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： Romain Saussard Boubker Bouzid Marius Vasiliu Roger Reynaud Renault S.A.S Guyancourt France SATIE Université Paris Saclay Orsay France

ISBN: (纸本)9781509035328

Today's vehicles increasingly embed software intelligence in order to be safer for the driver, and to achieve autonomous driving in a close future. To answer the computational needs of these algorithms, system-on-chip (SoC) suppliers propose heterogeneous architectures. With such complex SoCs, embedding applications in vehicle becomes more and more complex for car manufacturers. Indeed, it is not trivial to find the best suited SoC for a given application, and to define load balancing strategies when working with heterogeneous architectures. These difficulties can be overcome by using performance prediction, based on computing architectures models. To build these models, we provide a set of test vectors which automatically extract key characteristics of tested architectures. Our methodology is able to perform a complete computing architecture model, by using 3 different levels of tests, each one characterizing a specific situation representative of real applications. We aim to obtain performance prediction for different applications, for any embedded SoCs based on models performed with this methodology. In this paper, we describe our characterization methodology, and show results obtained with embedded SoCs used for automotive applications.

关键词： computer architecture Kernel graphics processing units Benchmark testing image processing Vehicles

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Parallel Graph Coloring for Manycore Architectures

Parallel Graph Coloring for Manycore Architectures

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International symposium on Parallel and Distributed processing (IPDPS)

作者： Mehmet Deveci Erik G Boman Karen D. Devine Sivasankaran Rajamanickam Sandia National Laboratories Albuquerque NM

ISBN: (纸本)9781509021413

Graph algorithms are challenging to parallelize on manycore architectures due to complex data dependencies and irregular memory access. We consider the well studied problem of coloring the vertices of a graph. In many applications it is important to compute a coloring with few colors in near-lineartime. In parallel, the optimistic (speculative) coloring method by Gebremedhin and Manne is the preferred approach but it needs to be modified for manycore architectures. We discuss a range of implementation issues for this vertex-based optimistic approach. We also propose a novel edge-based optimistic approach that has more parallelism and is better suited to GPUs. We study the performance empirically on two architectures(Xeon Phi and GPU) and across many data sets (from finite element problems to social networks). Our implementation uses the Kokkos library, so it is portable across platforms. We show that on GPUs, we significantly reduce the number of colors (geometric mean 4X, but up to 48X) as compared to the widely used cuSPARSE library. In addition, our edge-based algorithm is 1.5 times faster on average than cuSPARSE, where it hasspeedups up to 139X on a circuit problem. We also show the effect of the coloring on a conjugate gradient solver using multi-colored Symmetric Gauss-Seidel method as preconditioner, the higher coloring quality found by the proposed methods reduces the overall solve time up to 33% compared to cuSPARSE.

关键词： Color Instruction sets computer architecture graphics processing units Algorithm design and analysis image color analysis Libraries

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Vis3D+: An Integrated System for GPU-Accelerated Volume image processing and Rendering 11th

Vis3D+: An Integrated System for GPU-Accelerated Volume Imag...

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11th International symposium on Visual Computing (ISVC)

作者： Nisar, I. McInerney, T. Ryerson Univ Dept Comp Sci Toronto ON M5B 2K3 Canada

ISBN: (纸本)9783319278575;9783319278568

This paper presents a prototype 3D image processing and rendering system that extends an existing interactive 3D image visualization system. The extensions consist of software modules implemented using graphics processing unit (GPU) programs known as "compute shaders". Compute shaders are able to utilize the massively parallel, general-purpose computing capabilities provided by modern GPUs and can also be tightly integrated as new stages in a GPU-based volume and surface rendering pipeline. The compute shaders in this paper are designed to support the execution of volume image processing algorithms, as well as to support the interactive editing of the algorithms' output. An example volume image processing algorithm known as level set segmentation is implemented and demonstrated. A new editing module is developed that enables user modification of the segmentation algorithm's output by extending a pre-existing volume "painting" interface.

关键词： graphics processing unit

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An image Degradation Model for Depth-augmented image Editing

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computer graphics FORUM 2015年第5期34卷 191-199页

作者： Hennessey, James W. Mitra, Niloy J. UCL London WC1E 6BT England

images remain the most popular medium to capture our surroundings. Although significant advances have been made in developing image editing tools, the key challenge is to intelligently account for missing depth information. The growing popularity of depth images offers a new avenue to revisit image editing tasks. In this work, we investigate how even coarse depth information can be exploited to address some of the fundamental challenges in image editing namely producing correct perspective, handling occlusion, and obtaining segmentation. To this end, we propose a novel image degradation model that predicts how well an image edit can be performed in presence of coarse depth information. Technically, we create proxy geometry to summarize available depth information, and use it to predict occlusions and ordering between image patches, complete occluded regions, and anticipate image-level changes under camera movement. We evaluate the proposed image degradation model in the context of parallax photography from single depth images.

关键词： PHOTOGRAPHY image analysis image denoising SEGMENTATION (image processing) CAMERA movement VIDEO tapes -- Editing

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Example Based Repetitive Structure Synthesis

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computer graphics FORUM 2015年第5期34卷 39-52页

作者： Roveri, Riccardo Oztireli, A. Cengiz Martin, Sebastian Solenthaler, Barbara Gross, Markus Swiss Fed Inst Technol Zurich Switzerland VirtaMed Res Zurich Switzerland

We present an example based geometry synthesis approach for generating general repetitive structures. Our model is based on a meshless representation, unifying and extending previous synthesis methods. Structures in the example and output are converted into a functional representation, where the functions are defined by point locations and attributes. We then formulate synthesis as a minimization problem where patches from the output function are matched to those of the example. As compared to existing repetitive structure synthesis methods, the new algorithm offers several advantages. It handles general discrete and continuous structures, and their mixtures in the same framework. The smooth formulation leads to employing robust optimization procedures in the algorithm. Equipped with an accurate patch similarity measure and dedicated sampling control, the algorithm preserves local structures accurately, regardless of the initial distribution of output points. It can also progressively synthesize output structures in given subspaces, allowing users to interactively control and guide the synthesis in real-time. We present various results for continuous/discrete structures and their mixtures, residing on curves, submanifolds, volumes, and general subspaces, some of which are generated interactively.

关键词： GEOMETRY TEXTURE analysis (image processing) computer graphics SUBMANIFOLDS MATHEMATICAL optimization MESHFREE methods (Numerical analysis)

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