The GeForce 6 series GPU architecture

Kilgariff, Emmett; Fernando, Randima

doi:10.1145/1198555.1198767

Cited by 38 publications

(21 citation statements)

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“…See e.g. [17,21] for details of NVIDIA's GeForce 6 series. Here we note that, a GPU contains several two dimensional arrays of pixels called buffers (or texture memory).…”

Section: Gpu Model Of Computationmentioning

confidence: 99%

Natural neighbor interpolation based grid DEM construction using a GPU

Beutel

Mølhave

Agarwal

2010

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

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With modern LiDAR technology the amount of topographic data, in the form of massive point clouds, has increased dramatically. One of the most fundamental GIS tasks is to construct a grid digital elevation model (DEM) from these 3D point clouds. In this paper we present a simple yet very fast algorithm for constructing a grid DEM from massive point clouds using natural neighbor interpolation (NNI). We use a graphics processing unit (GPU) to significantly speed up the computation. To handle the large data sets and to deal with graphics hardware limitations clever blocking schemes are used to partition the point cloud. For example, using standard desktop computers and graphics hardware, we construct a high-resolution grid with 150 million cells from two billion points in less than thirty-seven minutes. This is about one-tenth of the time required for the same computer to perform a standard linear interpolation, which produces a much less smooth surface.

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“…See e.g. [17,21] for details of NVIDIA's GeForce 6 series. Here we note that, a GPU contains several two dimensional arrays of pixels called buffers (or texture memory).…”

Section: Gpu Model Of Computationmentioning

confidence: 99%

Natural neighbor interpolation based grid DEM construction using a GPU

Beutel

Mølhave

Agarwal

2010

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

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“…These output pixels are then sent to screen to produce the 3D image. Kilgariff et al [4] have conducted a detailed study of the architecture of the series 6 graphics cards from NVidia. The architecture of GPUs is based on carrying out operations on textures using a set of internal processors known as vertex and fragment processors.…”

Section: Graphical Processing Unitsmentioning

confidence: 99%

“…For this example we will use 100 fitness cases and the parameters we will use are described in table 1. In addition, we also use a second symbolic regression problem described by equation (4) for which it is harder to find a solution:…”

Section: Symbolic Regressionmentioning

confidence: 99%

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A data parallel approach to genetic programming using programmable graphics hardware

Chitty

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community as low cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor architectures which can be achieved by harnessing the computing power of these next generation graphics cards.

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“…Processor architectures specifically aimed at stream programming workloads have also Engine (BE) [9], Nvidia GeForce series [10], Ageia's PhysX [22], TI TMS320C6472 [21] and many DSPs.…”

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confidence: 99%

Dynamic scheduling of stream programs on embedded multi-core processors

Lee

Che

Chatha

2012

Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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Stream computing has emerged as an important model of computation for embedded system applications particularly in the multimedia and network processing domains. In recent past several programming languages and embedded multi-core processors have been proposed for streaming applications. This thesis examines the execution and dynamic scheduling of stream programs on embedded multi-core processors. The thesis addresses the problem in the context of a multi-tasking environment with a time varying allocation of processing elements for a particular streaming application. As a solution the thesis proposes a two step approach where the stream program is compiled to gather key application information, and to generate re-targetable code. A light weight dynamic scheduler incorporates the second stage of the approach. The dynamic scheduler utilizes the static information and available resources to assign or partition the application across the multi-core architecture. The objective of the dynamic scheduler is to maximize the throughput of the application, and it is sensitive to the resource (processing elements, scratch-pad memory, DMA bandwidth) constraints imposed by the target architecture. We evaluate the proposed approach by compiling and scheduling benchmark stream programs on a representative embedded multi-core processor. We present experimental results that evaluate the quality of the solutions generated by the proposed approach by comparisons with existing techniques.

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The GeForce 6 series GPU architecture

Cited by 38 publications

References 0 publications

Natural neighbor interpolation based grid DEM construction using a GPU

Natural neighbor interpolation based grid DEM construction using a GPU

A data parallel approach to genetic programming using programmable graphics hardware

Dynamic scheduling of stream programs on embedded multi-core processors

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