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About 6,032 papers for “Graphics processing unit”
Patricia J. Bishop, Vice Provost, Dean Of Graduate Studies et al. · 2003
The committee, the college and the University of Central Florida are not liable for any use of the materials presented in this study. INTERACTIVE GLOBAL ILLUMINATION ON THE
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National Cancer Institute · Definitions · 2020
Cited by 1Open access
Maciej Chociej, Supervised Karl Jenkins · 2007
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2008
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Edward J. Wyrwas · NASA STI Repository (National Aeronautics and Space Administration) · 2018
This presentation will include information about Graphics Processing Unit (GPU) technology, NASA Electronic Parts and Packaging (NEPP) tasks and their purpose, collaborations, a roadmap, NEPP partners, results to date, and future plans.
Cited by 0Open access
Tadej Ciglarič · 2016
Efficient data compression algorithms can be slow. The purpose of this work is an attempt of efficient parallelization of compression algorithm for execution on graphics processing units. Since graphics processing units contain an efficient parallel computing unit, it is reasonable to expect speedup from such parallelization of the algorithm. This work contains an overview of deflate algorithm and its existing parallel implementations intended for graphics processing units. We sequentially implemented the algorithm and parallelized it in two different ways using OpenCL framework. The implementations were tested on a corpus of files, intended for testing of compression algorithms. We compared the results with existing sequential and parallel implementations.
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T.A. Loughry · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2023
Systems and methods for decompressing compressed data that has been compressed by way of a lossless compression algorithm are described herein. In a general embodiment, a graphics processing unit (GPU) is programmed to receive compressed data packets and decompress such packets in parallel. The compressed data packets are compressed representations of an image, and the lossless compression algorithm is a Rice compression algorithm.
Cited by 0Open access
Jianfu Zhou, Xiaoguang Liu, Douglas S. Stones et al. · Bioinformatics · 2011
GPU MrBayes (i.e. the proposed implementation of MrBayes MC³ for the GPU) is available from http://mrbayes-gpu.sourceforge.net/.
Cited by 62
McDougal, Matthew, Ragasa, Scott, Russell, Sam · NASA STI Repository (National Aeronautics and Space Administration)
Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often greater, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques.
Cited by 0Open access
Akihiro Hirano, Kenji Nakayama · 2016
Abstract This paper presents efficient implementa-tion of RLS-based adaptive filters with a large number of taps on nVIDIA GeForce graphics processing unit (GPU) and CUDA software development environment. Modification of the order and the combination of calcu-lations reduces the number of accesses to slow off-chip memory. Assigning tasks into multiple threads also takes memory access order into account. Multiple shader pro-cessor arrays are used to handle a large matrix. For a 8192-tap case, a GPU program is almost 30-times faster than a CPU program. Real-time processing is possible for an 8kHz-sampling and 512-tap case by us-ing 32 shader processors, which is only 25 % of GeForce 8800GTS. 1
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2014
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Craig M. Wittenbrink, Emmett Kilgariff, Arjun Prabhu · 2010
This article consists of a collection of slides from the author's conference presentation on the NVIDIA Fermi GF family of GPU products. Some of the specific topics discussed include: the special features, system specifications, and system design for these products; system architectures; applications for use; platforms supported; processing capabilities; memory capabilities; and targeted markets.
Cited by 39
Russell, Sam, McDougal, Matthew, Ragasa, Scott · NASA STI Repository (National Aeronautics and Space Administration)
Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often great, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques. Technical Methodology/Approach: Apply massively parallel algorithms and data structures to the specific analysis requirements presented when working with thermographic data sets.
Cited by 0
J. George Cherian Panappally, M S Dhanesh · 2014
This work describes the designing of a Graphics Processing unit that deals with image processing. Graphics Processing Unit (GPU) is an important factor when it comes to large computing. Images and videos that are having large data can be processed efficiently in GPU by exploiting its feature of parallel execution. Digital image processing implemented on hardware provides higher processing speed and performance. The use of Verilog HDL for the design of GPU provides an immediate implementation possibility. The paper focuses on image processing operations like Brightness manipulation, Contrast manipulation, image cropping, image zooming, image rotation and morphological operators such as Dilation and Erosion.
Cited by 4
Lingjuan Wu, Liang Huang, Xiong Tinggang · 2017
Graphics Processing Unit (GPU) performs graphics computing and its architecture has developed from the fixed function pipeline to the programmable unified pipeline. Unified architecture promises dynamic load balancing and guarantees the high parallel computing of GPU. This paper presents the design and implementation of a unified architecture GPU. The unified shader is based on the SIMD and SIMT architecture. On the thread level, SIMT guarantees the full-load capability of unified shader by thread managing and scheduling. On the instruction level, SIMD controls the execution of the unified shader hardware unit. We finish the algorithm, architecture design and Verilog RTL implementation. The verification results on FPGA show that the proposed GPU works correctly and its vertex and fragment processing speed reaches one unit per clock cycle.
Cited by 1Open access
Ziyi Liu, Wenjing Ma · 2008
With recent technological advances, graphics processing units (GPUs) are providing increasingly higher performance with improvement programmability. This paper investigates NVIDIApsilas CUDA technology that enables data mining algorithm be parallelized effectively on GPU. The proposed algorithm exploits the computational power and the memory hierarchy of GPUs, using the shared memory to store frequently accessed data. Experimental results indicate that the speed of the computation through the GPU is considerably faster than through the CPU.
Cited by 10
Catherine A. McIntosh · UA Campus Repository (The University of Arizona) · 2019
Group project with Kray Althaus, Josue Ortiz, Kevin Siruno, and Sebastian Thiem
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Christopher Davis · 2005
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Vincent Roberge, Mohammed Tarbouchi, Francis A. Okou · IEEE Transactions on Smart Grid · 2015
Power distribution networks operate in a radial topology, but also include extra tie switches to allow for their reconfiguration in case of scheduled maintenance or unexpected failure. With the implementation of the smart grid and the development of fast high power switching devices, it is now possible to automatize this reconfiguration to also adjust to demand fluctuation and always operate the network in the optimal topology, minimizing power transmission losses. This automation requires the development of highly efficient and powerful optimization algorithms that can compute the optimal configuration with minimum delay. This paper presents a parallel genetic algorithm on graphics processing unit for distribution feeder reconfiguration. By exploiting the massively parallel architecture of graphics processors, the execution time of the solver is reduced by a factor of 66.2×, resulting in a very fast solver. Moreover, the metaheuristic uses a unique solution encoding based on the minimum spanning tree to maintain the radial structure of the candidate topologies. This novel encoding drastically improves the effectiveness of the genetic algorithm and allows for the optimal reconfiguration of networks up to 4400 buses; five times larger than any of the references surveyed.
Cited by 25
Matt Nantais, Colin Novak, Jeff Defoe · Canadian acoustics · 2006
The change in acoustic characteristics in personal computers to console gaming and home entertainment systems with the change in the Graphics Processing Unit (GPU), is presented. The tests are carried out using identical configurations of the software and system hardware. The prime components of the hardware used in the project are central processing unit, motherboard, hard disc drive, memory, power supply, optical drive, and additional cooling system. The results from the measurements taken for each GPU tested are analyzed and compared. The test results are obtained using a photo tachometer and reflective tape adhered to one particular fan blade. The test shows that loudness is a psychoacoustic metric developed by Zwicker and Fastal that aims to quantify how loud a sound is perceived as compared to a standard sound. The acoustic experiment reveals that the inherent noise generation mechanism increases with the increase of the complexity of the cooling solution.
Cited by 2Open access