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Journal articles on the topic 'GPU Systems'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 September 2023

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1

Jararweh, Yaser, Moath Jarrah, and Abdelkader Bousselham. "GPU Scaling." International Journal of Information Technology and Web Engineering 9, no. 4 (2014): 13–23. http://dx.doi.org/10.4018/ijitwe.2014100102.

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Current state-of-the-art GPU-based systems offer unprecedented performance advantages through accelerating the most compute-intensive portions of applications by an order of magnitude. GPU computing presents a viable solution for the ever-increasing complexities in applications and the growing demands for immense computational resources. In this paper the authors investigate different platforms of GPU-based systems, starting from the Personal Supercomputing (PSC) to cloud-based GPU systems. The authors explore and evaluate the GPU-based platforms and the authors present a comparison discussion

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2

Dematte, L., and D. Prandi. "GPU computing for systems biology." Briefings in Bioinformatics 11, no. 3 (2010): 323–33. http://dx.doi.org/10.1093/bib/bbq006.

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3

Ban, Zhihua, Jianguo Liu, and Jeremy Fouriaux. "GMMSP on GPU." Journal of Real-Time Image Processing 17, no. 2 (2018): 245–57. http://dx.doi.org/10.1007/s11554-018-0762-3.

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4

Georgii, Joachim, and Rüdiger Westermann. "Mass-spring systems on the GPU." Simulation Modelling Practice and Theory 13, no. 8 (2005): 693–702. http://dx.doi.org/10.1016/j.simpat.2005.08.004.

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5

Huynh, Huynh Phung, Andrei Hagiescu, Ong Zhong Liang, Weng-Fai Wong, and Rick Siow Mong Goh. "Mapping Streaming Applications onto GPU Systems." IEEE Transactions on Parallel and Distributed Systems 25, no. 9 (2014): 2374–85. http://dx.doi.org/10.1109/tpds.2013.195.

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6

Deniz, Etem, and Alper Sen. "MINIME-GPU." ACM Transactions on Architecture and Code Optimization 12, no. 4 (2016): 1–25. http://dx.doi.org/10.1145/2818693.

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7

Braak, Gert-Jan Van Den, and Henk Corporaal. "R-GPU." ACM Transactions on Architecture and Code Optimization 13, no. 1 (2016): 1–24. http://dx.doi.org/10.1145/2890506.

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8

INO, Fumihiko, Shinta NAKAGAWA, and Kenichi HAGIHARA. "GPU-Chariot: A Programming Framework for Stream Applications Running on Multi-GPU Systems." IEICE Transactions on Information and Systems E96.D, no. 12 (2013): 2604–16. http://dx.doi.org/10.1587/transinf.e96.d.2604.

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9

Rosenfeld, Viktor, Sebastian Breß, and Volker Markl. "Query Processing on Heterogeneous CPU/GPU Systems." ACM Computing Surveys 55, no. 1 (2023): 1–38. http://dx.doi.org/10.1145/3485126.

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Due to their high computational power and internal memory bandwidth, graphic processing units (GPUs) have been extensively studied by the database systems research community. A heterogeneous query processing system that employs CPUs and GPUs at the same time has to solve many challenges, including how to distribute the workload on processors with different capabilities; how to overcome the data transfer bottleneck; and how to support implementations for multiple processors efficiently. In this survey we devise a classification scheme to categorize techniques developed to address these challeng

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Besozzi, Daniela, Giulio Caravagna, Paolo Cazzaniga, Marco Nobile, Dario Pescini, and Alessandro Re. "GPU-powered Simulation Methodologies for Biological Systems." Electronic Proceedings in Theoretical Computer Science 130 (September 30, 2013): 87–91. http://dx.doi.org/10.4204/eptcs.130.14.

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11

ODAKA, Fumihiro, and Kenkichi SATO. "S2030201 GPU Computing Systems: History and Application." Proceedings of Mechanical Engineering Congress, Japan 2014 (2014): _S2030201——_S2030201—. http://dx.doi.org/10.1299/jsmemecj.2014._s2030201-.

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12

Maza, Marc Moreno, and Wei Pan. "Solving Bivariate Polynomial Systems on a GPU." Journal of Physics: Conference Series 341 (February 9, 2012): 012022. http://dx.doi.org/10.1088/1742-6596/341/1/012022.

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13

ODAGAWA, Masato, Yuriko TAKESHIMA, Issei FUJISHIRO, Gota KIKUGAWA, and Taku OHARA. "GPU-Based Adaptive Visualization for Particle Systems." TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series B 77, no. 781 (2011): 1767–78. http://dx.doi.org/10.1299/kikaib.77.1767.

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14

Maza, Marc Moreno, and Wei Pan. "Solving bivariate polynomial systems on a GPU." ACM Communications in Computer Algebra 45, no. 1/2 (2011): 127–28. http://dx.doi.org/10.1145/2016567.2016589.

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15

Jiang, Hai, Yi Chen, Zhi Qiao, Kuan-Ching Li, WonWoo Ro, and Jean-Luc Gaudiot. "Accelerating MapReduce framework on multi-GPU systems." Cluster Computing 17, no. 2 (2013): 293–301. http://dx.doi.org/10.1007/s10586-013-0276-5.

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16

Bernaschi, M., M. Fatica, G. Parisi, and L. Parisi. "Multi-GPU codes for spin systems simulations." Computer Physics Communications 183, no. 7 (2012): 1416–21. http://dx.doi.org/10.1016/j.cpc.2012.02.015.

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17

Ino, Fumihiko, Akihiro Ogita, Kentaro Oita, and Kenichi Hagihara. "Cooperative multitasking for GPU-accelerated grid systems." Concurrency and Computation: Practice and Experience 24, no. 1 (2011): 96–107. http://dx.doi.org/10.1002/cpe.1722.

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18

Lamas-Rodríguez, Julián, Dora B. Heras, Francisco Argüello, Dagmar Kainmueller, Stefan Zachow, and Montserrat Bóo. "GPU-accelerated level-set segmentation." Journal of Real-Time Image Processing 12, no. 1 (2013): 15–29. http://dx.doi.org/10.1007/s11554-013-0378-6.

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19

Meng, Wanwan, Yongguang Cheng, Jiayang Wu, Zhiyan Yang, Yunxian Zhu, and Shuai Shang. "GPU Acceleration of Hydraulic Transient Simulations of Large-Scale Water Supply Systems." Applied Sciences 9, no. 1 (2018): 91. http://dx.doi.org/10.3390/app9010091.

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Simulating hydraulic transients in ultra-long water (oil, gas) transmission or large-scale distribution systems are time-consuming, and exploring ways to improve the simulation efficiency is an essential research direction. The parallel implementation of the method of characteristics (MOC) on graphics processing unit (GPU) chips is a promising approach for accelerating the simulations, because GPU has a great parallelization ability for massive but simple computations, and the explicit and local features of MOC meet the features of GPU quite well. In this paper, we propose and verify a GPU imp

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20

Zhou, Zhe, Wenrui Diao, Xiangyu Liu, Zhou Li, Kehuan Zhang, and Rui Liu. "Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU." Proceedings on Privacy Enhancing Technologies 2017, no. 2 (2017): 57–73. http://dx.doi.org/10.1515/popets-2017-0016.

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Abstract According to previous reports, information could be leaked from GPU memory; however, the security implications of such a threat were mostly over-looked, because only limited information could be indirectly extracted through side-channel attacks. In this paper, we propose a novel algorithm for recovering raw data directly from the GPU memory residues of many popular applications such as Google Chrome and Adobe PDF reader. Our algorithm enables harvesting highly sensitive information including credit card numbers and email contents from GPU memory residues. Evaluation results also indic

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21

Campeanu, Gabriel, and Mehrdad Saadatmand. "A Two-Layer Component-Based Allocation for Embedded Systems with GPUs." Designs 3, no. 1 (2019): 6. http://dx.doi.org/10.3390/designs3010006.

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Component-based development is a software engineering paradigm that can facilitate the construction of embedded systems and tackle its complexities. The modern embedded systems have more and more demanding requirements. One way to cope with such a versatile and growing set of requirements is to employ heterogeneous processing power, i.e., CPU–GPU architectures. The new CPU–GPU embedded boards deliver an increased performance but also introduce additional complexity and challenges. In this work, we address the component-to-hardware allocation for CPU–GPU embedded systems. The allocation for suc

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22

Chen, Yong, Hai Jin, Han Jiang, Dechao Xu, Ran Zheng, and Haocheng Liu. "Implementation and Optimization of GPU-Based Static State Security Analysis in Power Systems." Mobile Information Systems 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/1897476.

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Static state security analysis (SSSA) is one of the most important computations to check whether a power system is in normal and secure operating state. It is a challenge to satisfy real-time requirements with CPU-based concurrent methods due to the intensive computations. A sensitivity analysis-based method with Graphics processing unit (GPU) is proposed for power systems, which can reduce calculation time by 40% compared to the execution on a 4-core CPU. The proposed method involves load flow analysis and sensitivity analysis. In load flow analysis, a multifrontal method for sparse LU factor

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23

Tran, Giang Son, Thi Phuong Nghiem, and Jean-Christophe Burie. "Fast parallel blur detection on GPU." Journal of Real-Time Image Processing 17, no. 4 (2018): 903–13. http://dx.doi.org/10.1007/s11554-018-0837-1.

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24

Abell, Stephen, Nhan Do, and John Jaehwan Lee. "GPU-OSDDA: a bit-vector GPU-based deadlock detection algorithm for single-unit resource systems." International Journal of Parallel, Emergent and Distributed Systems 31, no. 5 (2015): 450–68. http://dx.doi.org/10.1080/17445760.2015.1100301.

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25

Abell, Stephen, Nhan Do, and John Jaehwan Lee. "GPU-LMDDA: a bit-vector GPU-based deadlock detection algorithm for multi-unit resource systems." International Journal of Parallel, Emergent and Distributed Systems 31, no. 6 (2016): 562–90. http://dx.doi.org/10.1080/17445760.2016.1140761.

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26

Wang, Long, Masaki Iwasawa, Keigo Nitadori, and Junichiro Makino. "petar: a high-performance N-body code for modelling massive collisional stellar systems." Monthly Notices of the Royal Astronomical Society 497, no. 1 (2020): 536–55. http://dx.doi.org/10.1093/mnras/staa1915.

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ABSTRACT The numerical simulations of massive collisional stellar systems, such as globular clusters (GCs), are very time consuming. Until now, only a few realistic million-body simulations of GCs with a small fraction of binaries ($5{{\ \rm per\ cent}}$) have been performed by using the nbody6++gpu code. Such models took half a year computational time on a Graphic Processing Unit (GPU)-based supercomputer. In this work, we develop a new N-body code, petar, by combining the methods of Barnes–Hut tree, Hermite integrator and slow-down algorithmic regularization. The code can accurately handle a

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27

Kopysov, S. P., A. K. Novikov, and Yu A. Sagdeeva. "Solving of discontinuous Galerkin method systems on GPU." Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, no. 4 (December 2011): 121–31. http://dx.doi.org/10.20537/vm110411.

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28

Martínez-del-Amor, Miguel A., Manuel García-Quismondo, Luis F. Macías-Ramos, Luis Valencia-Cabrera, Agustin Riscos-Núñez, and Mario J. Pérez-Jiménez. "Simulating P Systems on GPU Devices: A Survey." Fundamenta Informaticae 136, no. 3 (2015): 269–84. http://dx.doi.org/10.3233/fi-2015-1157.

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29

van Pelt, Roy, Anna Vilanova, and Huub van de Wetering. "Illustrative Volume Visualization Using GPU-Based Particle Systems." IEEE Transactions on Visualization and Computer Graphics 16, no. 4 (2010): 571–82. http://dx.doi.org/10.1109/tvcg.2010.32.

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30

Anzt, Hartwig, Stanimire Tomov, Mark Gates, Jack Dongarra, and Vincent Heuveline. "Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems." Procedia Computer Science 9 (2012): 7–16. http://dx.doi.org/10.1016/j.procs.2012.04.002.

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31

Galiano, V., H. Migallón, V. Migallón, and J. Penadés. "GPU-based parallel algorithms for sparse nonlinear systems." Journal of Parallel and Distributed Computing 72, no. 9 (2012): 1098–105. http://dx.doi.org/10.1016/j.jpdc.2011.10.016.

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32

Nere, Andrew, Sean Franey, Atif Hashmi, and Mikko Lipasti. "Simulating cortical networks on heterogeneous multi-GPU systems." Journal of Parallel and Distributed Computing 73, no. 7 (2013): 953–71. http://dx.doi.org/10.1016/j.jpdc.2012.02.006.

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33

Mastrostefano, Enrico, and Massimo Bernaschi. "Efficient breadth first search on multi-GPU systems." Journal of Parallel and Distributed Computing 73, no. 9 (2013): 1292–305. http://dx.doi.org/10.1016/j.jpdc.2013.05.007.

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34

Acosta, Alejandro, Vicente Blanco, and Francisco Almeida. "Dynamic load balancing on heterogeneous multi-GPU systems." Computers & Electrical Engineering 39, no. 8 (2013): 2591–602. http://dx.doi.org/10.1016/j.compeleceng.2013.08.004.

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35

Dastgeer, Usman, and Christoph Kessler. "Performance-aware composition framework for GPU-based systems." Journal of Supercomputing 71, no. 12 (2014): 4646–62. http://dx.doi.org/10.1007/s11227-014-1105-1.

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36

Jo, Heeseung, Seung-Tae Hong, Jae-Woo Chang, and Dong Hoon Choi. "Offloading data encryption to GPU in database systems." Journal of Supercomputing 69, no. 1 (2014): 375–94. http://dx.doi.org/10.1007/s11227-014-1159-0.

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37

Vuduc, Richard, and Kent Czechowski. "What GPU Computing Means for High-End Systems." IEEE Micro 31, no. 4 (2011): 74–78. http://dx.doi.org/10.1109/mm.2011.78.

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38

da Silva Junior, Jose Ricardo, Esteban Clua, and Leonardo Murta. "Efficient image-aware version control systems using GPU." Software: Practice and Experience 46, no. 8 (2015): 1011–33. http://dx.doi.org/10.1002/spe.2340.

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39

Gembris, Daniel, Markus Neeb, Markus Gipp, Andreas Kugel, and Reinhard Männer. "Correlation analysis on GPU systems using NVIDIA’s CUDA." Journal of Real-Time Image Processing 6, no. 4 (2010): 275–80. http://dx.doi.org/10.1007/s11554-010-0162-9.

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40

YOO, SEUNG-HUN, and CHANG-SUNG JEONG. "IMAGE REGISTRATION AND FUSION SYSTEM BASED ON GPU." Journal of Circuits, Systems and Computers 19, no. 01 (2010): 173–89. http://dx.doi.org/10.1142/s0218126610006049.

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Graphics processing unit (GPU) has surfaced as a high-quality platform for computer vision-related systems. In this paper, we propose a straightforward system consisting of a registration and a fusion method over GPU, which generates good results at high speed, compared to non-GPU-based systems. Our GPU-accelerated system utilizes existing methods through converting the methods into the GPU-based platform. The registration method uses point correspondences to find a registering transformation estimated with the incremental parameters in a coarse-to-fine way, while the fusion algorithm uses mul

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41

Kumar, Anshuman, Pablo R. Arantes, Aakash Saha, Giulia Palermo, and Bryan M. Wong. "GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems." Molecules 28, no. 3 (2023): 1277. http://dx.doi.org/10.3390/molecules28031277.

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Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculati

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42

Ngo, Long Thanh, Dzung Dinh Nguyen, Long The Pham, and Cuong Manh Luong. "Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation." Advances in Fuzzy Systems 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/698062.

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As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of r

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43

Ding, Yifan, Nicholas Botzer, and Tim Weninger. "HetSeq: Distributed GPU Training on Heterogeneous Infrastructure." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 15432–38. http://dx.doi.org/10.1609/aaai.v35i17.17813.

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Modern deep learning systems like PyTorch and Tensorflow are able to train enormous models with billions (or trillions) of parameters on a distributed infrastructure. These systems require that the internal nodes have the same memory capacity and compute performance. Unfortunately, most organizations, especially universities, have a piecemeal approach to purchasing computer systems resulting in a heterogeneous infrastructure, which cannot be used to compute large models. The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability

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44

Fu, Yaosheng, Evgeny Bolotin, Niladrish Chatterjee, David Nellans, and Stephen W. Keckler. "GPU Domain Specialization via Composable On-Package Architecture." ACM Transactions on Architecture and Code Optimization 19, no. 1 (2022): 1–23. http://dx.doi.org/10.1145/3484505.

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As GPUs scale their low-precision matrix math throughput to boost deep learning (DL) performance, they upset the balance between math throughput and memory system capabilities. We demonstrate that a converged GPU design trying to address diverging architectural requirements between FP32 (or larger)-based HPC and FP16 (or smaller)-based DL workloads results in sub-optimal configurations for either of the application domains. We argue that a C omposable O n- PA ckage GPU (COPA-GPU) architecture to provide domain-specialized GPU products is the most practical solution to these diverging requireme

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45

Rapaport, D. C. "GPU molecular dynamics: Algorithms and performance." Journal of Physics: Conference Series 2241, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2241/1/012007.

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Abstract A previous study of MD algorithms designed for GPU use is extended to cover more recent developments in GPU architecture. Algorithm modifications are described, togther with extensions to more complex systems. New measurements include the effects of increased parallelism on GPU performance, as well as comparisons with multiple-core CPUs using multitasking based on CPU threads and message passing. The results show that the GPU retains a significant performance advantage.

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46

Zhu, Rui, Chang Nian Chen, and Lei Hua Qin. "An Transfer Latency Optimized Solution in GPU-Accelerated De-Duplication." Applied Mechanics and Materials 336-338 (July 2013): 2059–62. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2059.

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Recently, GPU has been introduced as an important tool in general purpose programming due to its powerful computing capacity. In data de-duplication systems, GPU has been used to accelerate the chunking and hashing algorithms. However, the data transfer latency between the memories of CPU to GPU is one of the main challenges in GPU accelerated de-duplication. To alleviate this challenge, our solution strives to reduce the data transfer time between host and GPU memory on parallelized content-defined chunking and hashing algorithm. In our experiment, it has shown 15%~20% performance improvement

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47

DeFrancisco, Richard, Shenghsun Cho, Michael Ferdman, and Scott A. Smolka. "Swarm model checking on the GPU." International Journal on Software Tools for Technology Transfer 22, no. 5 (2020): 583–99. http://dx.doi.org/10.1007/s10009-020-00576-x.

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48

Wang, Qihan, Zhen Peng, Bin Ren, Jie Chen, and Robert G. Edwards. "MemHC: An Optimized GPU Memory Management Framework for Accelerating Many-body Correlation." ACM Transactions on Architecture and Code Optimization 19, no. 2 (2022): 1–26. http://dx.doi.org/10.1145/3506705.

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The many-body correlation function is a fundamental computation kernel in modern physics computing applications, e.g., Hadron Contractions in Lattice quantum chromodynamics (QCD). This kernel is both computation and memory intensive, involving a series of tensor contractions, and thus usually runs on accelerators like GPUs. Existing optimizations on many-body correlation mainly focus on individual tensor contractions (e.g., cuBLAS libraries and others). In contrast, this work discovers a new optimization dimension for many-body correlation by exploring the optimization opportunities among tens

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49

Zhang, Yu, Da Peng, Xiaofei Liao, et al. "LargeGraph." ACM Transactions on Architecture and Code Optimization 18, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3477603.

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Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system LargeGraph . Different from existing out-of-GPU-memory systems, LargeGraph proposes a dependency-aware data-driven execution approach , which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and als

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50

Wong, Un-Hong, Takayuki Aoki, and Hon-Cheng Wong. "Efficient magnetohydrodynamic simulations on distributed multi-GPU systems using a novel GPU Direct–MPI hybrid approach." Computer Physics Communications 185, no. 7 (2014): 1901–13. http://dx.doi.org/10.1016/j.cpc.2014.03.018.

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