Thematische Bibliographien / GPU1

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „GPU1“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 7. Februar 2022

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Zeitschriftenartikel zum Thema "GPU1"

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Nakada, Yuji, and Yoshifumi Itoh. "Pseudomonas aeruginosa PAO1 genes for 3-guanidinopropionate and 4-guanidinobutyrate utilization may be derived from a common ancestor." Microbiology 151, no. 12 (2005): 4055–62. http://dx.doi.org/10.1099/mic.0.28258-0.

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Pseudomonas aeruginosa PAO1 utilizes 3-guanidinopropionate (3-GP) and 4-guanidinobutyrate (4-GB), which differ in one methylene group only, via distinct enzymes: guanidinopropionase (EC 3.5.3.17; the gpuA product) and guanidinobutyrase (EC 3.5.3.7; the gbuA product). The authors cloned and characterized the contiguous gpuPAR genes (in that order) responsible for 3-GP utilization, and compared the deduced sequences of their putative protein products, and the potential regulatory mechanisms of gpuPA, with those of the corresponding gbu genes encoding the 4-GB catabolic system. GpuA and GpuR have
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Guo, Sen, San Feng Chen, and Yong Sheng Liang. "Global Shared Memory Design for Multi-GPU Graphics Cards on Personal Supercomputer." Applied Mechanics and Materials 263-266 (December 2012): 1236–41. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1236.

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When programming CUDA or OpenCL on multi-GPU systems, the programmers usually expect the GPUs on the same system can communicate fast with each other. For instance, they hope a device memory copy from GPU1s memory to GPU2s memory can be done inside the graphics card, and needn’t to employ the PCIE, which is in relative low speed. In this paper, we propose an idea to add a multi-channel memory to the multi-GPU board, and this memory is only for transferring data between different GPUs. This multi-channel memory should have multiple interfaces, including one common interface shared by different
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Palmer, Daniel A., Jill K. Thompson, Lie Li, Ashton Prat та Ping Wang. "Gib2, A Novel Gβ-like/RACK1 Homolog, Functions as a Gβ Subunit in cAMP Signaling and Is Essential in Cryptococcus neoformans". Journal of Biological Chemistry 281, № 43 (2006): 32596–605. http://dx.doi.org/10.1074/jbc.m602768200.

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Canonical G proteins are heterotrimeric, consisting of α, β, and γ subunits. Despite multiple Gα subunits functioning in fungi, only a single Gβ subunit per species has been identified, suggesting that non-conventional G protein signaling exists in this diverse group of eukaryotic organisms. Using the Gα subunit Gpa1 that functions in cAMP signaling as bait in a two-hybrid screen, we have identified a novel Gβ-like/RACK1 protein homolog, Gib2, from the human pathogenic fungus Cryptococcus neoformans. Gib2 contains a seven WD-40 repeat motif and is predicted to form a seven-bladed β propeller s
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Harashima, Toshiaki, та Joseph Heitman. "Gα Subunit Gpa2 Recruits Kelch Repeat Subunits That Inhibit Receptor-G Protein Coupling during cAMP-induced Dimorphic Transitions in Saccharomyces cerevisiae". Molecular Biology of the Cell 16, № 10 (2005): 4557–71. http://dx.doi.org/10.1091/mbc.e05-05-0403.

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All eukaryotic cells sense extracellular stimuli and activate intracellular signaling cascades via G protein-coupled receptors (GPCR) and associated heterotrimeric G proteins. The Saccharomyces cerevisiae GPCR Gpr1 and associated Gα subunit Gpa2 sense extracellular carbon sources (including glucose) to govern filamentous growth. In contrast to conventional Gα subunits, Gpa2 forms an atypical G protein complex with the kelch repeat Gβ mimic proteins Gpb1 and Gpb2. Gpb1/2 negatively regulate cAMP signaling by inhibiting Gpa2 and an as yet unidentified target. Here we show that Gpa2 requires lipi
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Lai, Jianqi, Hua Li, Zhengyu Tian, and Ye Zhang. "A Multi-GPU Parallel Algorithm in Hypersonic Flow Computations." Mathematical Problems in Engineering 2019 (March 17, 2019): 1–15. http://dx.doi.org/10.1155/2019/2053156.

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Computational fluid dynamics (CFD) plays an important role in the optimal design of aircraft and the analysis of complex flow mechanisms in the aerospace domain. The graphics processing unit (GPU) has a strong floating-point operation capability and a high memory bandwidth in data parallelism, which brings great opportunities for CFD. A cell-centred finite volume method is applied to solve three-dimensional compressible Navier–Stokes equations on structured meshes with an upwind AUSM+UP numerical scheme for space discretization, and four-stage Runge–Kutta method is used for time discretization
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Wang, Ping, John R. Perfect та Joseph Heitman. "The G-Protein β Subunit GPB1 Is Required for Mating and Haploid Fruiting in Cryptococcus neoformans". Molecular and Cellular Biology 20, № 1 (2000): 352–62. http://dx.doi.org/10.1128/mcb.20.1.352-362.2000.

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ABSTRACT Cryptococcus neoformans is an opportunistic fungal pathogen with a defined sexual cycle. The gene encoding a heterotrimeric G-protein β subunit, GPB1, was cloned and disrupted.gpb1 mutant strains are sterile, indicating a role for this gene in mating. GPB1 plays an active role in mediating responses to pheromones in early mating steps (conjugation tube formation and cell fusion) and signals via a mitogen-activated protein (MAP) kinase cascade in both MATα and MATa cells. The functions of GPB1 are distinct from those of the Gα protein GPA1, which functions in a nutrient-sensing cyclic
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Zhou, Chao, and Tao Zhang. "High Performance Graph Data Imputation on Multiple GPUs." Future Internet 13, no. 2 (2021): 36. http://dx.doi.org/10.3390/fi13020036.

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In real applications, massive data with graph structures are often incomplete due to various restrictions. Therefore, graph data imputation algorithms have been widely used in the fields of social networks, sensor networks, and MRI to solve the graph data completion problem. To keep the data relevant, a data structure is represented by a graph-tensor, in which each matrix is the vertex value of a weighted graph. The convolutional imputation algorithm has been proposed to solve the low-rank graph-tensor completion problem that some data matrices are entirely unobserved. However, this data imput
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MITTAL, SPARSH. "A SURVEY OF TECHNIQUES FOR MANAGING AND LEVERAGING CACHES IN GPUs." Journal of Circuits, Systems and Computers 23, no. 08 (2014): 1430002. http://dx.doi.org/10.1142/s0218126614300025.

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Initially introduced as special-purpose accelerators for graphics applications, graphics processing units (GPUs) have now emerged as general purpose computing platforms for a wide range of applications. To address the requirements of these applications, modern GPUs include sizable hardware-managed caches. However, several factors, such as unique architecture of GPU, rise of CPU–GPU heterogeneous computing, etc., demand effective management of caches to achieve high performance and energy efficiency. Recently, several techniques have been proposed for this purpose. In this paper, we survey seve
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Oden, Lena, and Holger Fröning. "InfiniBand Verbs on GPU: a case study of controlling an InfiniBand network device from the GPU." International Journal of High Performance Computing Applications 31, no. 4 (2015): 274–84. http://dx.doi.org/10.1177/1094342015588142.

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Due to their massive parallelism and high performance per Watt, GPUs have gained high popularity in high-performance computing and are a strong candidate for future exascale systems. But communication and data transfer in GPU-accelerated systems remain a challenging problem. Since the GPU normally is not able to control a network device, a hybrid-programming model is preferred whereby the GPU is used for calculation and the CPU handles the communication. As a result, communication between distributed GPUs suffers from unnecessary overhead, introduced by switching control flow from GPUs to CPUs
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Gaurav and Steven F. Wojtkiewicz. "Use of GPU Computing for Uncertainty Quantification in Computational Mechanics: A Case Study." Scientific Programming 19, no. 4 (2011): 199–212. http://dx.doi.org/10.1155/2011/730213.

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Graphics processing units (GPUs) are rapidly emerging as a more economical and highly competitive alternative to CPU-based parallel computing. As the degree of software control of GPUs has increased, many researchers have explored their use in non-gaming applications. Recent studies have shown that GPUs consistently outperform their best corresponding CPU-based parallel computing alternatives in single-instruction multiple-data (SIMD) strategies. This study explores the use of GPUs for uncertainty quantification in computational mechanics. Five types of analysis procedures that are frequently
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Dissertationen zum Thema "GPU1"

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Stodůlka, Martin. "Akcelerace ultrazvukových simulací pomocí multi-GPU systémů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445538.

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The main focus of this project is usage of multi - GPU systems and usage of CUDA unified memory . Its goal is to accelerate computation of 2D and 3D FFT, which is the main part of simulations in k- Wave library .K- Wave is a C++/ Matlab library used for simulations of propagation of ultrasonic waves in 1D , 2D or 3D space . Acceleration of these functions is necessary , because the simulations are computationally intensive .
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Ma, Wenjing. "Automatic Transformation and Optimization of Applications on GPUs and GPU clusters." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1300972089.

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Tanasić, Ivan. "Towards multiprogrammed GPUs." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/405796.

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Programmable Graphics Processing Units (GPUs) have recently become the most pervasitheve massively parallel processors. They have come a long way, from fixed function ASICs designed to accelerate graphics tasks to a programmable architecture that can also execute general-purpose computations. Because of their performance and efficiency, an increasing amount of software is relying on them to accelerate data parallel and computationally intensive sections of code. They have earned a place in many systems, from low power mobile devices to the biggest data centers in the world. However, GPUs are s
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Hong, Changwan. "Code Optimization on GPUs." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1557123832601533.

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Wang, Kaibo. "Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1447685368.

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Pedersen, Stian Aaraas. "Progressive Photon Mapping on GPUs." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22995.

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Physically based rendering using ray tracing is capable of producing realistic images of much higher quality than other methods. However, the computational costs associated with exploring all paths of light are huge; it can take hours to render high quality images of complex scenes. Using graphics processing units has emerged as a popular way to speed up this process. The recent appearance of libraries like Nvidia's CUDA and OptiX make the processing power of modern GPUs more available than ever before.This project includes an overview of current photon mapping techniques. We present a co
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Harb, Mohammed. "Quantum transport modeling with GPUs." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114417.

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In this thesis, we have developed a parallel GPU accelerated code for carrying out transport calculations within the Non-Equilibrium Green's Function (NEGF) framework using the Tight-Binding (TB) model. We also discuss the theoretical, modelling, and computational issues that arise in this implementation. We demonstrate that a heterogenous implementation with CPUs and GPUs is superior to single processor, multiple processor, and massively parallel CPU-only implementations. The GPU-Matlab Interface (GMI) developed in this work for use in our NEGF-TB code is not application specific and can be u
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Hovland, Rune Johan. "Throughput Computing on Future GPUs." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9893.

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<p>The general-purpose computing capabilities of the Graphics Processing Unit (GPU) have recently been given a great deal of attention by the High-Performance Computing (HPC) community. By allowing massively parallel applications to run efficiently on commodity graphics cards, ”personal supercomputers” are now available in desktop versions at a low price. For some applications, speedups of 70 times that of a single CPU implementation have been achieved. Among the most popular GPUs are those based on the NVIDIA Tesla Architecture which allows relatively easy development of GPU applications usin
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Kim, Jinsung. "Optimizing Tensor Contractions on GPUs." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563237825735994.

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Tadros, Rimon. "Accelerating web search using GPUs." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54722.

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The amount of content on the Internet is growing rapidly as well as the number of the online Internet users. As a consequence, web search engines need to increase their computing capabilities and data continually while maintaining low search latency and without a significant rise in the cost per query. To serve this larger numbers of online users, web search engines utilize a large distributed system in the data centers. They partition their data across several hundred of thousands of independent commodity servers called Index Serving Nodes (ISNs). These ISNs work together to serve
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Bücher zum Thema "GPU1"

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Kindratenko, Volodymyr, ed. Numerical Computations with GPUs. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06548-9.

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GPU computing gems. Morgan Kaufmann, 2011.

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Engel, Wolfgang. GPU Pro 360. Edited by Wolfgang Engel. A K Peters/CRC Press, 2018. http://dx.doi.org/10.1201/9781351052108.

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Engel, Wolfgang. GPU Pro 360. Edited by Wolfgang Engel. A K Peters/CRC Press, 2018. http://dx.doi.org/10.1201/9781351208352.

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Engel, Wolfgang. GPU Pro 360. Edited by Wolfgang Engel. A K Peters/CRC Press, 2018. http://dx.doi.org/10.1201/9781351261524.

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Engel, Wolfgang, ed. GPU Pro 360. A K Peters/CRC Press, 2018. http://dx.doi.org/10.1201/b22483.

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Designing scientific applications on GPUs. CRC/Taylor & Francis, 2014.

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Rodengen, Jeffrey L. The legacy of GPU. Write Stuff Enterprises, 2000.

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Cai, Yiyu, and Simon See, eds. GPU Computing and Applications. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-134-3.

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GPU Pro2: Advanced rendering techniques. AK Peters, 2011.

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Buchteile zum Thema "GPU1"

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Reinders, James, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, and Xinmin Tian. "Programming for GPUs." In Data Parallel C++. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5574-2_15.

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Abstract Over the last few decades, Graphics Processing Units (GPUs) have evolved from specialized hardware devices capable of drawing images on a screen to general-purpose devices capable of executing complex parallel kernels. Nowadays, nearly every computer includes a GPU alongside a traditional CPU, and many programs may be accelerated by offloading part of a parallel algorithm from the CPU to the GPU.
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Osama, Muhammad, Anton Wijs, and Armin Biere. "SAT Solving with GPU Accelerated Inprocessing." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_8.

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AbstractSince 2013, the leading SAT solvers in the SAT competition all use inprocessing, which unlike preprocessing, interleaves search with simplifications. However, applying inprocessing frequently can still be a bottle neck, i.e., for hard or large formulas. In this work, we introduce the first attempt to parallelize inprocessing on GPU architectures. As memory is a scarce resource in GPUs, we present new space-efficient data structures and devise a data-parallel garbage collector. It runs in parallel on the GPU to reduce memory consumption and improves memory access locality. Our new parallel variable elimination algorithm is twice as fast as previous work. In experiments our new solver ParaFROST solves many benchmarks faster on the GPU than its sequential counterparts.
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Andrzejewski, Witold, and Robert Wrembel. "GPU-WAH: Applying GPUs to Compressing Bitmap Indexes with Word Aligned Hybrid." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15251-1_26.

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Lombardi, Luca, and Piercarlo Dondi. "GPU." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1308.

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Ketkar, Nikhil. "Introduction to GPUs." In Deep Learning with Python. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2766-4_10.

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Kalé, Laxmikant V., Abhinav Bhatele, Eric J. Bohm, et al. "NVIDIA GPU." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_276.

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Uelschen, Michael. "GPU-Programmierung." In Software Engineering Paralleler Systeme. Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-25343-1_6.

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Rauber, Thomas, and Gudula Rünger. "GPU-Programmierung." In Parallele Programmierung. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-13604-7_7.

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Ranta, Sunil Mohan, Jag Mohan Singh, and P. J. Narayanan. "GPU Objects." In Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_32.

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Vázquez, Fransisco, José Antonio Martínez, and Ester M. Garzón. "GPU Computing." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_998.

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Konferenzberichte zum Thema "GPU1"

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Tarashima, Shuhei, Satoshi Someya, and Koji Okamoto. "Acceleration of Recursive Cross-Correlation PIV Using Multiple GPUs." In ASME/JSME 2011 8th Thermal Engineering Joint Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajtec2011-44442.

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A large number of PIV algorithms and systems have been proposed, many of which are highly sophisticated in terms of accuracy and spatial and temporal resolution. However, a general problem with PIV is the time cost to compute vector fields from images, which often imposes specific constraints on the measurement methods. In this paper, focusing on recursive direct cross-correlation PIV with window deformation, which is one of the most popular algorithms for PIV, we propose a technique to accelerate PIV processing using a single Graphics Processing Unit (single-GPU) and multiple GPUs (multi-GPU)
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Ferro, Mariza, André Yokoyama, Vinicius Klõh, et al. "Analysis of GPU Power Consumption Using Internal Sensors." In XVI Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2017. http://dx.doi.org/10.5753/wperformance.2017.3360.

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GPUs has been widely used in scientific computing, as by offering exceptional performance as by power-efficient hardware. Its position established in high-performance and scientific computing communities has increased the urgency of understanding the power cost of GPU usage in accurate measurements. For this, the use of internal sensors are extremely important. In this work, we employ the GPU sensors to obtain high-resolution power profiles of real and benchmark applications. We wrote our own tools to query the sensors of two NVIDIA GPUs from different generations and compare the accuracy of t
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Santos, Ricardo, Rhayssa Sonohata, Casio Krebs, et al. "Exploração do Projeto de Sistemas Baseados em GPU ciente de Dark Silicon." In XX Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2019. http://dx.doi.org/10.5753/wscad.2019.8682.

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Este artigo propõe uma infraestrutura para realizar a exploração do espaço de projetos de sistemas computacionais com unidades de processamento gráfico (GPUs) em conjunto com núcleos para processamento de propósito geral, com o objetivo de reduzir dark silicon e aumentar o desempenho do sistema em tempo de projeto. A ferramenta GPGPUSim de simulação e estimativa fı́sica de projeto foi estendida para realizar estimativas de dark silicon das plataformas de GPUs e, em seguida, foi integrada ao framework MultiExplorer. Adicionalmente, foi desenvolvida uma estratégia para estimativa de de
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Gisbert, Fernando, Roque Corral, and Guillermo Pastor. "Implementation of an Edge-Based Navier-Stokes Solver for Unstructured Grids in Graphics Processing Units." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46224.

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The implementation of an edge-based three-dimensional RANS equations solver for unstructured grids that runs on both central processing units (CPUs) and graphics processing units (GPUs) is presented. This CPU/GPU duality is kept without double-writing the code, reducing programming and maintenance costs. The GPU implementation is based on the standard OpenCL language. The code has been parallelized using MPI. Some turbomachinery benchmark cases are presented. For all cases, an order of magnitude reduction in computational time is achieved when the code is executed on GPUs instead of CPUs.
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Konobrytskyi, Dmytro, Thomas Kurfess, Joshua Tarbutton, and Tommy Tucker. "GPGPU Accelerated 3-Axis CNC Machining Simulation." In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1096.

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GPUs (Graphics Processing Units), traditionally used for 3D graphics calculations, have recently got an ability to perform general purpose calculations with a GPGPU (General Purpose GPU) technology. Moreover, GPUs can be much faster than CPUs (Central Processing Units) by performing hundreds or even thousands commands concurrently. This parallel processing allows the GPU achieving the extremely high performance but also requires using only highly parallel algorithms which can provide enough commands on each clock cycle. This work formulates a methodology for selection of a right geometry repre
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Gajjar, Mrugesh, Christian Amann, and Kai Kadau. "High-Performance Computing Probabilistic Fracture Mechanics Implementation for Gas Turbine Rotor Disks on Distributed Architectures Including Graphics Processing Units (GPUs)." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59295.

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Abstract We present an efficient Monte Carlo based probabilistic fracture mechanics simulation implementation for heterogeneous high-performance (HPC) architectures including CPUs and GPUs. The specific application focuses on large heavy-duty gas turbine rotor components for the energy sector. A reliable probabilistic risk quantification requires the simulation of millions to billions of Monte Carlo (MC) samples. We apply a modified Runge-Kutta algorithm in order to solve numerically the fatigue crack growth for this large number of cracks for varying initial crack sizes, locations, material a
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Romanelli, G., L. Mangani, E. Casartelli, A. Gadda, and M. Favale. "Implementation of Explicit Density-Based Unstructured CFD Solver for Turbomachinery Applications on Graphical Processing Units." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43396.

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For the aerodynamic design of multistage compressors and turbines Computational Fluid Dynamics (CFD) plays a fundamental role. In fact it allows the characterization of the complex behaviour of turbomachinery components with high fidelity. Together with the availability of more and more powerful computing resources, current trends pursue the adoption of such high-fidelity tools and state-of-the-art technology even in the preliminary design phases. Within such a framework Graphical Processing Units (GPUs) yield further growth potential, allowing a significant reduction of CFD process turn-aroun
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Bergmann, Ryan M., and Jasmina L. Vujić. "Monte Carlo Neutron Transport on GPUs." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-30148.

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GPUs have gradually increased in computational power from the small, job-specific boards of the early 90s to the programmable powerhouses of today. Compared to CPUs, they have a higher aggregate memory bandwidth, much higher floating-point operations per second (FLOPS), and lower energy consumption per FLOP. Because one of the main obstacles in exascale computing is power consumption, many new supercomputing platforms are gaining much of their computational capacity by incorporating GPUs into their compute nodes. Since CPU optimized parallel algorithms are not directly portable to GPU architec
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Breder, Bernardo, Eduardo Charles, Rommel Cruz, Esteban Clua, Cristiana Bentes, and Lucia Drummond. "Maximizando o Uso dos Recursos de GPU Através da Reordenação da Submissão de Kernels Concorrentes." In XVII Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/wscad.2016.14264.

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O aumento da quantidade de recursos disponíveis nas GPUs modernas despertou um novo interesse no problema do compartilhamento de seus recursos por diferentes kernels. A nova geração de GPUs permite a execução simultânea de kernels, porém ainda são limitadas ao fato de que decisões de escalonamento são tomadas pelo hardware em tempo de execução. Tais decisões dependem da ordem em que os kernels são submetidos para execução, criando execuções onde a GPU não necessariamente está com a melhor taxa de ocupação. Neste trabalho, apresentamos uma proposta de otimização para reordenar a submissão de ke
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Brandvik, Tobias, and Graham Pullan. "An Accelerated 3D Navier-Stokes Solver for Flows in Turbomachines." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-60052.

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A new three-dimensional Navier-Stokes solver for flows in turbomachines has been developed. The new solver is based on the latest version of the Denton codes, but has been implemented to run on Graphics Processing Units (GPUs) instead of the traditional Central Processing Unit (CPU). The change in processor enables an order-of-magnitude reduction in run-time due to the higher performance of the GPU. Scaling results for a 16 node GPU cluster are also presented, showing almost linear scaling for typical turbomachinery cases. For validation purposes, a test case consisting of a three-stage turbin
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Berichte der Organisationen zum Thema "GPU1"

1

Holladay, Daniel. Non-LTE Opacity Computation on GPUs. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1148954.

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2

Long, Alex Roberts. Jayenne GPU Strategy Update. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1634935.

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3

Hughes, Clayton, Simon Hammond, Mengchi Zhang, Yechen Liu, Tim Rogers, and Robert Hoekstra. SST-GPU: A Scalable SST GPU Component for Performance Modeling and Profiling. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1762830.

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4

Gong, Qian, Wenji Wu, and Phil DeMar. GoldenEye:Stream-Based Network Packet Inspection using GPUs. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1508017.

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5

Cawkwell, Marc J., Anders M. Niklasson, and Susan M. Mniszewski. Quantum molecular dynamics on parallel GPUs: w13_qmdgpu. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1131015.

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6

Monroe, Laura Marie, Sarah E. Michalak, and Joanne R. Wendelberger. Randomized selection on the GPU. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1090658.

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7

Monroe, Laura Marie, Sarah E. Michalak, and Joanne R. Wendelberger. Randomized selection on the GPU. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1090659.

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8

Kennedy, Liz Sexton, and Philippe Canal. Geant Exascale / GPU Pilot Project. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1599619.

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9

Monroe, Laura M. GPGPU Computing and Visualization on GPUs at LANL. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1053889.

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10

Boggan, Sha'Kia, and Daniel M. Pressel. GPUs: An Emerging Platform for General-Purpose Computation. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada471188.

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