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A fast GPU algorithm for graph connectivity

A fast GPU algorithm for graph connectivity

引用

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

作者： Jyothish Soman Kothapalli Kishore P J Narayanan IIIT-Hyderabad Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424465330

graphics processing units provide a large computational power at a very low price which position them as an ubiquitous accelerator. General purpose programming on the graphics processing units (GPGPU) is best suited for regular data parallel algorithms. They are not directly amenable for algorithms which have irregular data access patterns such as list ranking, and finding the connected components of a graph, and the like. In this work, we present a GPU-optimized implementation for finding the connected components of a given graph. Our implementation tries to minimize the impact of irregularity, both at the data level and functional level. Our implementation achieves a speed up of 9 to 12 times over the best sequential CPU implementation. For instance, our implementation finds connected components of a graph of 10 million nodes and 60 million edges in about 500 milliseconds on a GPU, given a random edge list. We also draw interesting observations on why PRAM algorithms, such as the Shiloach-Vishkin algorithm may not be a good fit for the GPU and how they should be modified.

关键词： Phase change random access memory Inference algorithms Partitioning algorithms Central Processing Unit parallel processing Arithmetic Pervasive computing parallel programming graphics parallel algorithms

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Improving the visual analysis of high-dimensional datasets using quality measures

Improving the visual analysis of high-dimensional datasets u...

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ieee symposium on Visual Analytics Science and Technology (VAST)

作者： Georgia Albuquerque Martin Eisemann Dirk J. Lehmann Holger Theisel Marcus Magnor Technical University of Braunschweig Germany University of Magdeburg Germany

Modern visualization methods are needed to cope with very high-dimensional data. Efficient visual analytical techniques are required to extract the information content in these data. The large number of possible projections for each method, which usually grow quadrat-ically or even exponentially with the number of dimensions, urges the necessity to employ automatic reduction techniques, automatic sorting or selecting the projections, based on their information-bearing content. Different quality measures have been successfully applied for several specified user tasks and established visualization techniques, like Scatterplots, Scatterplot Matrices or parallel Coordinates. Many other popular visualization techniques exist, but due to the structural differences, the measures are not directly applicable to them and new approaches are needed. In this paper we propose new quality measures for three popular visualization methods: Radviz, Pixel-Oriented Displays and Table Lenses. Our experiments show that these measures efficiently guide the visual analysis task.

关键词： Pixel data visualization visualization Noise Lenses Image color analysis Clustering algorithms

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CUDA-based AES parallelization with fine-tuned GPU memory utilization

CUDA-based AES parallelization with fine-tuned GPU memory ut...

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

作者： Chonglei Mei Hai Jiang Jeff Jenness Department of Computer Science Arkansas State University USA

ISBN: (纸本)9781424465330

Current graphics Processing Unit (GPU) presents large potentials in speeding up computationally intensive data parallel applications over traditional parallelization approaches since there are much more hardware threads inside GPUs than the computational cores available to common CPU threads. NVIDIA developed a generic GPU programming platform, CUDA, which allows programmers to utilize GPU through C programming language and parallelize applications in a similar way as in traditional multithreading approach. However, not all applications are suitable for this new platform. Only computationally intensive applications without strong dependency are good candidates. Although Advanced Encryption Standard (AES) does not belong to this group due to the light workload in its efficient implementation, this paper proposed an approach to arrange data in different GPU memory spaces properly, overcoming the extra communication delay, and still turning GPU into an effective accelerator. Experimental results have demonstrated its effectiveness by performance gains and proved that GPU can be used to accelerate more types of applications.

关键词： Concurrent computing graphics processing unit Hardware Central Processing Unit parallel programming Programming profession Computer languages Multithreading Computer applications

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An improved visual inspection system using visual servo

An improved visual inspection system using visual servo

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ieee/SICE International symposium on System Integration

作者： Chuantao Zang Koichi Hashimoto Graduate School of Information Sciences University of Tohoku Japan

In this paper we present an improved automatic visual inspection system. In this system, homography based visual servo is used to accurately locate the camera position and attitude so that a template matching inspection can be realized. To improve the visual servo system's performance, we propose a combination strategy of a GPU based Efficient Second-order Minimization (GPU-ESM) algorithm and a GPU based Scale Invariant Feature Transform (SIFT) algorithm. By utilizing the parallel processing capability of a GPU, system processing speed and reliability has been greatly improved. In this paper, the implementation details of the visual inspection system are presented. The performance of the proposed combination strategy is evaluated with experimental data.

关键词： visualization graphics processing unit Servomotors Cameras Kernel Inspection Tracking

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An implicit and GPU based approach for solving cracks in terrain visualization

An implicit and GPU based approach for solving cracks in ter...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wei Cao Fuzhou Duan Huili Gong Wenji Zhao Key Laboratory of 3D Information Acquisition and Application Ministry of Education Beijing China Capital Normal University Beijing China

The huge storage for large terrain datasets and high resolution images have been the most important factors that influence the performance for interactive large-scale terrain rendering. To solve the problem, researchers partition a whole terrain into small tiles and utilize GPU (graphics Processing Unit) to render them respectively. But usually the different levels of detail between adjacent tiles will induce cracks between them which will decrease the fidelity greatly. Many algorithms have presented solutions to overcome the disadvantage but haven't achieved absolutely success either by the complexity or by the bad effect. In this paper we present an implicit and GPU-based method to overcome the traditional drawbacks. We will partition the square terrain datasets into triangular tiles and predefine the amount of the inner triangles of each tile to 2 m ×2 m (m>1). Then at runtime, we force the maximum level difference of the adjacent tiles to 1. And this will connect the terrain tiles seamlessly. And in the paper we transform the DEM data into vertex texture, and store the data in GPU. And finally we use the advanced shader language HLSL to implement the operation of fetching height value. The above improvements have achieved great effect.

关键词： Tiles Rendering (computer graphics) graphics processing unit Partitioning algorithms Real time systems Algorithm design and analysis Spatial resolution

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Predata – preparatory data analytics on peta-scale machines

PreDatA – preparatory data analytics on peta-scale machines

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Fang Zheng Hasan Abbasi Ciprian Docan Jay Lofstead Qing Liu Scott Klasky Manish Parashar Norbert Podhorszki Karsten Schwan Matthew Wolf College of Computing Georgia Institute of Technology Atlanta GA USA Center of Autonomic Computing Rutgers University Piscataway NJ USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)9781424464425

Peta-scale scientific applications running on High End Computing (HEC) platforms can generate large volumes of data. For high performance storage and in order to be useful to science end users, such data must be organized in its layout, indexed, sorted, and otherwise manipulated for subsequent data presentation, visualization, and detailed analysis. In addition, scientists desire to gain insights into selected data characteristics `hidden' or `latent' in these massive datasets while data is being produced by simulations. Predata, short for Preparatory data Analytics, is an approach to preparing and characterizing data while it is being produced by the large scale simulations running on peta-scale machines. By dedicating additional compute nodes on the machine as `staging' nodes and by staging simulations' output data through these nodes, Predata can exploit their computational power to perform select data manipulations with lower latency than attainable by first moving data into file systems and storage. Such intransit manipulations are supported by the Predata middleware through asynchronous data movement to reduce write latency, application-specific operations on streaming data that are able to discover latent data characteristics, and appropriate data reorganization and metadata annotation to speed up subsequent data access. Predata enhances the scalability and flexibility of the current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and inspection, as well as for data exchange between concurrently running simulations.

关键词： data analysis Computational modeling Analytical models Delay data visualization Performance analysis large-scale systems File systems Middleware Scalability

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Fast ultrasound beam prediction for linear and regular two-dimensional arrays

Fast ultrasound beam prediction for linear and regular two-d...

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作者： Hlawitschka, Mario McGough, Robert J. Ferrara, Katherine W. Kruse, Dustin E. Institute for Data Analysis and Visualization University of California Davis CA 95616 United States Department of Electrical and Computer Engineering Michigan State University East Lansing MI 48824 United States Department of Biomedical Engineering University of California Davis CA 95616 United States

ISBN: (纸本)9781457703829

Real-time beam predictions are desirable for ultrasound therapy guidance, as treatment planning requires individualized computations. To address the long-standing issue of the computational burden associated with calculating the acoustic field in large volumes, we use modern graphics processing units to accelerate the calculation of near-field pressure fields for therapeutic ultrasound arrays. First, we accelerate field computations for single rectangular pistons by employing the Fast Near-field Method (FNM) to accurately and efficiently estimate the complex near-field wave patterns in homogeneous media, and results are compared to the Rayleigh-Sommerfeld method. We then compute beam patterns of 1D and 2D piston arrays using pre-calculated beam patterns from a single piston. Our results show that these algorithms can benefit greatly from GPU computing hardware, exceeding a modern CPU by a factor of over 100. For a single rectangular piston, the FNM is able to calculate a 0.01% accurate field within 30 ns per field point. Furthermore, we demonstrate calculation speeds for arrays of up to 11.5 109 field points per second. Beam volumes containing 2563 field points are calculated within one second for arrays containing up to 512 pistons, thus, facilitating future real-time thermal dose predictions. © 2010 ieee.

关键词： Pistons

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Visualizing complex dynamics in many-core accelerator architectures

Visualizing complex dynamics in many-core accelerator archit...

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ieee International symposium on Performance Analysis of Systems and Software

作者： Aaron Ariel Wilson W. L. Fung Andrew E. Turner Tor M. Aamodt University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781424460236;9781424460229

While many-core accelerator architectures, such as today's graphics Processing Units (GPUs), offer orders of magnitude more raw computing power than contemporary CPUs, their massive parallelism often produces complex dynamic behaviors even with the simplest applications. Using a fixed set of hardware or simulator performance counters to quantify behavior over a large interval of time such as an entire application execution run or program phase may not capture this behavior. Software and/or hardware designers may consequently miss out on opportunities to optimize for better performance. Similarly, significant effort may be expended to find metrics that explain anomalous behavior in architecture design studies. Moreover, the increasing complexity of applications developed for today's GPU has created additional difficulties for software developers when attempting to identify bottlenecks of an application for optimization. This paper presents a novel GPU performance visualization tool, AerialVision, to address these two problems. It interfaces with the GPGPU-Sim simulator to capture and visualize the dynamic behavior of a GPU architecture throughout an application run. Similar to existing performance analysis tools for CPUs, it can annotate individual lines of source code with performance statistics to simplify the bottleneck identification process. To provide further insight, AerialVision introduces a novel methodology to relate pathological dynamic architectural behaviors resulting in performance loss with the part of the source code that is responsible. By rapidly providing insight into complex dynamic behavior, AerialVision enables research on improving many-core accelerator architectures and will help ensure applications written for these architectures reach their full performance potential.

关键词： visualization Accelerator architectures Application software Hardware Computer architecture graphics Concurrent computing parallel processing Computational modeling Counting circuits

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Optimization of linked list prefix computations on multithreaded GPUs using CUDA

Optimization of linked list prefix computations on multithre...

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International symposium on parallel and Distributed Processing (IPDPS)

作者： Zheng Wei Joseph JaJa Department of Electrical and Computer Engineering Institute of Advanced Computer Studies University of Maryland College Park USA

ISBN: (纸本)9781424464425

We present a number of optimization techniques to compute prefix sums on linked lists and implement them on multithreaded GPUs using CUDA. Prefix computations on linked structures involve in general highly irregular fine grain memory accesses that are typical of many computations on linked lists, trees, and graphs. While the current generation of GPUs provides substantial computational power and extremely high bandwidth memory accesses, they may appear at first to be primarily geared toward streamed, highly data parallel computations. In this paper, we introduce an optimized multithreaded GPU algorithm for prefix computations through a randomization process that reduces the problem to a large number of fine-grain computations. We map these fine-grain computations onto multithreaded GPUs in such a way that the processing cost per element is shown to be close to the best possible. Our experimental results show scalability for list sizes ranging from 1M nodes to 256M nodes, and significantly improve on the recently published parallel implementations of list ranking, including implementations on the Cell Processor, the MTA-8, and the NVIDIA GeForce 200 series. They also compare favorably to the performance of the best known CUDA algorithm for the scan operation on the Tesla C1060.

关键词： Concurrent computing parallel algorithms Multicore processing parallel processing graphics processing unit Scalability Hardware Tree graphs Educational institutions Phase change random access memory

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Using GPUs to improve system performance in visual servo systems

Using GPUs to improve system performance in visual servo sys...

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2010 ieee/RSJ International Conference on Intelligent Robots and Systems

作者： Chuantao Zang Koichi Hashimoto Graduate School of Information Sciences University of Tohoku Sendai Japan

This paper describes our novel work of using GPUs to improve the performance of a homography-based visual servo system. We present our novel implementations of a GPU based Efficient Second-order Minimization (GPU-ESM) algorithm. By utilizing the tremendous parallel processing capability of a GPU, we have obtained significant acceleration over its CPU counterpart. Currently our GPU-ESM algorithm can process a 360×360 pixels tracking area at 145 fps on a NVIDIA GTX295 board and Intel Core i7 920, approximately 30 times faster than a CPU implementation. This speedup substantially improves the realtime performance of our system. System reliability and stability are also greatly enhanced by a GPU based Scale Invariant Feature Transform (SIFT) algorithm, which is used to deal with such cases where ESM tracking failure happens, such as due to large image difference, occlusion and so on. In this paper, translation details of the ESM algorithm from CPU to GPU implementation and novel optimizations are presented. The co-processing model of multiple GPUs and multiple CPU threads is described in this paper. The performance of our GPU accelerated system is evaluated with experimental data.

关键词： graphics processing unit Kernel Instruction sets visualization Pixel Servomotors Least squares approximation

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