Relevant bibliographies by topics / Cloud GPUs

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cloud GPUs'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Cloud GPUs"

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Jo, Heeseung, Jinkyu Jeong, Myoungho Lee, and Dong Hoon Choi. "Exploiting GPUs in Virtual Machine for BioCloud." BioMed Research International 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/939460.

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Recently, biological applications start to be reimplemented into the applications which exploit many cores of GPUs for better computation performance. Therefore, by providing virtualized GPUs to VMs in cloud computing environment, many biological applications will willingly move into cloud environment to enhance their computation performance and utilize infinite cloud computing resource while reducing expenses for computations. In this paper, we propose a BioCloud system architecture that enables VMs to use GPUs in cloud environment. Because much of the previous research has focused on the sha
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Munk, Rasmus, David Marchant, and Brian Vinter. "Cloud enabling educational platforms with corc." CTE Workshop Proceedings 8 (March 19, 2021): 438–57. http://dx.doi.org/10.55056/cte.299.

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In this paper, it is shown how teaching platforms at educational institutions can utilize cloud platforms to scale a particular service, or gain access to compute instances with accelerator capability such as GPUs. Specifically at the University of Copenhagen (UCPH), it is demonstrated how the internal JupyterHub service, named Data Analysis Gateway (DAG), could utilize compute resources in the Oracle Cloud Infrastructure (OCI). This is achieved by utilizing the introduced Cloud Orchestrator (corc) framework, in conjunction with the novel JupyterHub spawner named MultipleSpawner. Through this
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Anzalone, Anna, Antonio Pagliaro, and Antonio Tutone. "An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications." Applied Sciences 14, no. 7 (2024): 2887. http://dx.doi.org/10.3390/app14072887.

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Cloud cover assessment is crucial for meteorology, Earth observation, and environmental monitoring, providing valuable data for weather forecasting, climate modeling, and remote sensing activities. Depending on the specific purpose, identifying and accounting for pixels affected by clouds is essential in spectral remote sensing imagery. In applications such as land monitoring and various remote sensing activities, detecting/removing cloud-contaminated pixels is crucial to ensuring the accuracy of advanced processing of satellite imagery. Typically, the objective of cloud masking is to produce
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Shi, X. "ELASTIC CLOUD COMPUTING ARCHITECTURE AND SYSTEM FOR HETEROGENEOUS SPATIOTEMPORAL COMPUTING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 115–19. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-115-2017.

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Spatiotemporal computation implements a variety of different algorithms. When big data are involved, desktop computer or standalone application may not be able to complete the computation task due to limited memory and computing power. Now that a variety of hardware accelerators and computing platforms are available to improve the performance of geocomputation, different algorithms may have different behavior on different computing infrastructure and platforms. Some are perfect for implementation on a cluster of graphics processing units (GPUs), while GPUs may not be useful on certain kind of
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Lin, Yu-Shiang, Chun-Yuan Lin, Hsiao-Chieh Chi, and Yeh-Ching Chung. "Multiple Sequence Alignments with Regular Expression Constraints on a Cloud Service System." International Journal of Grid and High Performance Computing 5, no. 3 (2013): 55–64. http://dx.doi.org/10.4018/jghpc.2013070105.

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Multiple sequence alignments with constraints are of priority concern in computational biology. Constrained sequence alignment incorporates the domain knowledge of biologists into sequence alignments such that the user-specified residues/segments are aligned together according to the alignment results. A series of constrained multiple sequence alignment tools have been developed in relevant literatures in the recent decade. GPU-REMuSiC is the most advanced method with the regular expression constraints, in which graphics processing units (GPUs) with CUDA are used. GPU-REMuSiC can achieve a spe
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Lahiff, Andrew, Shaun de Witt, Miguel Caballer, Giuseppe La Rocca, Stanislas Pamela, and David Coster. "Running HTC and HPC applications opportunistically across private, academic and public clouds." EPJ Web of Conferences 245 (2020): 07032. http://dx.doi.org/10.1051/epjconf/202024507032.

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The Fusion Science Demonstrator in the European Open Science Cloud for Research Pilot Project aimed to demonstrate that the fusion community can make use of distributed cloud resources. We developed a platform, Prominence, which enables users to transparently exploit idle cloud resources for running scientific workloads. In addition to standard HTC jobs, HPC jobs such as multi-node MPI are supported. All jobs are run in containers to ensure they will reliably run anywhere and are reproduceable. Cloud infrastructure is invisible to users, as all provisioning, including extensive failure handlin
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Lulla, Karan, Reena Chandra, and Karthik Sirigiri. "Proxy-Based Thermal and Acoustic Evaluation of Cloud GPUs for AI Training Workloads." American Journal of Applied Sciences 7, no. 07 (2025): 111–27. https://doi.org/10.37547/tajas/volume07issue07-12.

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The use of cloud-based Graphics Processing Units (GPUs) to train and deploy Deep Learning models has grown rapidly in importance, with the demand to learn more about their thermal and acoustic behavior under real-world workloads. A normal cloud cannot make direct telemetry like temperature, fan speed, or acoustic emissions. To overcome such shortcomings, this study quantifies GPU workloads' thermal and acoustic output with a proxy-based model derived from available metrics such as GPU utilization, memory provisioning, power consumption, and empirical Thermal Design Power (TDP) values. They com
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Liang, Tyng-Yeu, and You-Jie Li. "A Mobile Cloud Computing System for Mathematical Computation." International Journal of Grid and High Performance Computing 7, no. 3 (2015): 36–53. http://dx.doi.org/10.4018/ijghpc.2015070103.

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This paper is aimed at proposing a mobile cloud computing system called M2C (Mobile Math Cloud). This system provides users with an APP to accelerate the execution of MATLAB instructions and scripts on their Android-based mobile devices by taking advantage of diverse processors including CPUs and GPUs available in clouds. On the other hand, it supports time-sharing license management to reduce the user time of waiting system services and increase the resource utilization of clouds. Moreover, it supports parallel computing and optimal resource configurations for maximizing the performance of us
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Rajeev Reddy Chevuri. "The Role of GPUs in Accelerating Machine Learning Workloads." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2676–84. https://doi.org/10.32628/cseit251127424.

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This article presents a comprehensive overview of Graphics Processing Units (GPUs) and their transformative role in accelerating machine learning workloads. Starting with an explanation of the fundamental architectural differences between GPUs and CPUs, the article explores how the parallel processing capabilities of GPUs enable dramatic improvements in training deep learning models. The discussion covers GPU applications across convolutional neural networks, transformer architectures, and multi-GPU training strategies. Beyond training, the article examines GPU acceleration in inference, scien
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Nguyen, Anh, Abraham Monrroy Cano, Masato Edahiro, and Shinpei Kato. "GPU-Accelerated 3D Normal Distributions Transform." Journal of Robotics and Mechatronics 35, no. 2 (2023): 445–59. http://dx.doi.org/10.20965/jrm.2023.p0445.

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The three-dimensional (3D) normal distributions transform (NDT) is a popular scan registration method for 3D point cloud datasets. It has been widely used in sensor-based localization and mapping applications. However, the NDT cannot entirely utilize the computing power of modern many-core processors, such as graphics processing units (GPUs), because of the NDT’s linear nature. In this study, we investigated the use of NVIDIA’s GPUs and their programming platform called compute unified device architecture (CUDA) to accelerate the NDT algorithm. We proposed a design and implementation of our GP
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Dissertations / Theses on the topic "Cloud GPUs"

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Orts-Escolano, Sergio. "A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs." Doctoral thesis, Universidad de Alicante, 2013. http://hdl.handle.net/10045/36484.

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The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-O
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Sreenibha, Reddy Byreddy. "Performance Metrics Analysis of GamingAnywhere with GPU accelerated Nvidia CUDA." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16846.

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The modern world has opened the gates to a lot of advancements in cloud computing, particularly in the field of Cloud Gaming. The most recent development made in this area is the open-source cloud gaming system called GamingAnywhere. The relationship between the CPU and GPU is what is the main object of our concentration in this thesis paper. The Graphical Processing Unit (GPU) performance plays a vital role in analyzing the playing experience and enhancement of GamingAnywhere. In this paper, the virtualization of the GPU has been concentrated on and is suggested that the acceleration of this
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Zaahid, Mohammed. "Performance Metrics Analysis of GamingAnywhere with GPU acceletayed NVIDIA CUDA using gVirtuS." Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16852.

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The modern world has opened the gates to a lot of advancements in cloud computing, particularly in the field of Cloud Gaming. The most recent development made in this area is the open-source cloud gaming system called GamingAnywhere. The relationship between the CPU and GPU is what is the main object of our concentration in this thesis paper. The Graphical Processing Unit (GPU) performance plays a vital role in analyzing the playing experience and enhancement of GamingAnywhere. In this paper, the virtualization of the GPU has been concentrated on and is suggested that the acceleration of this
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Gasparetto, Thomas. "Développement d'une ferme informatique utilisant le Cloud Computing sur GPU." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALY011.

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Tous les travaux de cette thèse ont été développés dans le contexte du Cherenkov Telescope Array (CTA), qui sera le principal observatoire de la prochaine génération pour l'astronomie gamma à très haute énergie au sol. Le plan de ce travail est d'utiliser les GPU et le Cloud Computing afin d'accélérer les tâches de calcul exigeantes, en développant et en optimisant les pipelines d'analyse de données.La thèse se compose de deux parties: la première est destinée à l'estimation des performances du CTA pour l'observation de phénomènes violents tels que ceux générant des sursauts de rayons gamma (G
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Horn, Stefan. "Simulations of complex atmospheric flows using GPUs - the model ASAMgpu -." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-187994.

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Die vorliegende Arbeit beschreibt die Entwicklung des hochauflösenden Atmosphärenmodells ASAMgpu. Dabei handelt es sich um ein sogenanntes Grobstrukturmodell bei dem gröbere Strukturen mit typischen Skalen von Deka- bis Kilometern in der atmosphärischen Grenzschicht explizit aufgelöst werden. Hochfrequentere Anteile und deren Dissipation müssen dabei entweder explizit mit einem Turbulenzmodell oder, wie im Falle des beschriebenen Modells, implizit behandelt werden. Dazu wurde der Advektionsoperator mit einem dissipativen Upwind-Verfahren dritter Ordnung diskretisiert. Das Modell beinhaltet ein
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Tarantilis, Georgios E. "Simulating clouds with procedural texturing techniques using the GPU." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FTarantilis.pdf.

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Thesis (M.S. in Modeling, Virtual Environments and Simulations (MOVES))--Naval Postgraduate School, Sept. 2004.<br>Thesis Advisor(s): Rudy Darken, Joe Sullivan. Includes bibliographical references (p. 53). Also available online.
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Yanes, Luis. "Haptic Interaction with 3D oriented point clouds on the GPU." Thesis, University of East Anglia, 2015. https://ueaeprints.uea.ac.uk/58556/.

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Real-time point-based rendering and interaction with virtual objects is gaining popularity and importance as different haptic devices and technologies increasingly provide the basis for realistic interaction. Haptic Interaction is being used for a wide range of applications such as medical training, remote robot operators, tactile displays and video games. Virtual object visualization and interaction using haptic devices is the main focus; this process involves several steps such as: Data Acquisition, Graphic Rendering, Haptic Interaction and Data Modification. This work presents a framework f
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José, Silva Leite Pedro. "Massively parallel nearest neighbors searches in dynamic point clouds on GPU." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/2356.

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Made available in DSpace on 2014-06-12T15:57:17Z (GMT). No. of bitstreams: 2 arquivo3157_1.pdf: 3737373 bytes, checksum: 7ca491f9a72f2e9cf51764a7acac3e3c (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico<br>Esta dissertação introduz uma estrutura de dados baseada em gride implementada em GPU. Ela foi desenvolvida para pesquisa dos vizinhos mais próximos em nuvens de pontos dinâmicas, de uma forma massivamente paralela. A implementação possui desempenho em tempo real e é ex
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Gleza, Jan. "Cloud computing v herním průmyslu." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-114054.

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This thesis analyzes current status of software distribution in gaming industry and its biggest challenges. Thesis also includes insight in completely different approach to the software distribution -- Cloud Gaming. In practical part are thoroughly tested existing solutions and done functionality analysis. Practical part also includes experiment which regards to building own platform using currently existing tools and it is followed by discussion of results.
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Correia, Brum Rafaela. "Multi-FedLS : A Scheduler of Federated Learning Applications in a Multi-Cloud Environment." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS539.pdf.

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L'apprentissage fédéré (AF) est un nouveau domaine de l'apprentissage machine distribué où l'apprentissage garantit la confidentialité des données. Chaque client a accès uniquement à son propre ensemble de données local et privé. Cette approche est attrayante dans divers domaines du savoir car elle permet à différentes institutions de collaborer sans partager leurs données confidentielles. Comme la quantité de données requises pour la formation a considérablement augmenté ces dernières années, la plupart des institutions ne peuvent pas se permettre des centres de données physiques pour stocker
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Books on the topic "Cloud GPUs"

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Sheppard, Andrew. GPUs in the Cloud. O'Reilly Media, Incorporated, 2011.

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Simulating Clouds with Procedural Texturing Techniques Using the GPU. Storming Media, 2004.

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Wang, Ge. Improvisation of the Masses. Edited by Benjamin Piekut and George E. Lewis. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199892921.013.15.

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The mass-scale adoption of modern mobile computing technology presents immense potential to reshape the way we engage one another socially, creatively, and musically. This article explores ad hoc music-making and improvisatory performance on a massive scale, leveraging personal interactive mobile instruments (e.g., via iPhones and iPads), location-awareness (via GPS), and the connective social potential of cloud computing. I investigate a new social/musical improvisatory context that doesn’t exist in any single location but as a network and community of anonymous but connected participants aro
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Book chapters on the topic "Cloud GPUs"

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Kurkure, Uday, Hari Sivaraman, and Lan Vu. "Machine Learning Using Virtualized GPUs in Cloud Environments." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67630-2_41.

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Cheng, Wangzhao, Fangyu Zheng, Wuqiong Pan, Jingqiang Lin, Huorong Li, and Bingyu Li. "Building Your Private Cloud Storage on Public Cloud Service Using Embedded GPUs." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01701-9_28.

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Mungiello, Innocenzo, and Francesco De Rosa. "Adaptive Modular Mapping to Reduce Shared Memory Bank Conflicts on GPUs." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49109-7_34.

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Jansen, Wouter, Dennis Laurijssen, Robin Kerstens, Walter Daems, and Jan Steckel. "In-Air Imaging Sonar Sensor Network with Real-Time Processing Using GPUs." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33509-0_67.

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Kasmi, Najlae, Mostapha Zbakh, and Amine Haouari. "Performance Analysis of Preconditioned Conjugate Gradient Solver on Heterogeneous (Multi-CPUs/Multi-GPUs) Architecture." In Cloud Computing and Big Data: Technologies, Applications and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97719-5_20.

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Mahmoudi, Sidi Ahmed, Mohammed Amine Belarbi, and Pierre Manneback. "Towards a Smart Exploitation of GPUs for Low Energy Motion Estimation Using Full HD and 4K Videos." In Cloud Computing and Big Data: Technologies, Applications and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97719-5_18.

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Montella, Raffaele, Giuseppe Coviello, Giulio Giunta, Giuliano Laccetti, Florin Isaila, and Javier Garcia Blas. "A General-Purpose Virtualization Service for HPC on Cloud Computing: An Application to GPUs." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31464-3_75.

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Li, Zhen, and Jiabin Yuan. "Quantum Computer Simulation on GPU Cluster Incorporating Data Locality." In Cloud Computing and Security. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68505-2_8.

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Cai, Xingquan, Jinhong Li, and Zhitong Su. "Efficient Mushroom Cloud Simulation on GPU." In Technologies for E-Learning and Digital Entertainment. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69736-7_74.

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Maciá-Lillo, Antonio, Tamai Ramírez, Higinio Mora, Antonio Jimeno-Morenilla, and José-Luis Sánchez-Romero. "GPU Cloud Architectures for Bioinformatic Applications." In Bioinformatics and Biomedical Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34953-9_6.

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Conference papers on the topic "Cloud GPUs"

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Hur, Yoonseo, and Kyungyong Lee. "CNN Training Latency Prediction Using Hardware Metrics on Cloud GPUs." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2024. http://dx.doi.org/10.1109/ccgrid59990.2024.00033.

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Nabavinejad, Seyed Morteza, Weiwei Jia, Shubbhi Taneja, and Tian Guo. "CarbonDIS: Carbon-Aware DNN Inference Scheduling on Heterogeneous GPUs." In 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2025. https://doi.org/10.1109/ccgrid64434.2025.00032.

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Li, Xinyi, Ang Li, Bo Fang, Katarzyna Swirydowicz, Ignacio Laguna, and Ganesh Gopalakrishnan. "Discovery of Floating-Point Differences Between NVIDIA and AMD GPUs." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2024. http://dx.doi.org/10.1109/ccgrid59990.2024.00083.

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Yan, Chen, Boyu Diao, Hangda Liu, Zhulin An, and Yongjun Xu. "A Nonlinear Hash-Based Optimization Method for SpMV on GPUs." In 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2025. https://doi.org/10.1109/ccgrid64434.2025.00053.

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Lipe, Ellie, Neel Karia, Connor Espenshade, Clifford Stein, Asser Tantawi, and Olivier Tardieu. "Energy Efficient Scheduling of AI/ML Workloads on Multi Instance Gpus with Dynamic Repartitioning." In 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2025. https://doi.org/10.1109/ccgrid64434.2025.00066.

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Hussain, Syed Hamza, Sunidhi S. Naik, Neha Miriam Thomas, Tarun S. Ramanujam, and Bhaskarjyoti Das. "Assessing the Impracticality of Training and Finetuning Large Language Models on Consumer GPUs: Limitations in Time, Scalability, and Efficiency." In 2024 6th International Communication Engineering and Cloud Computing Conference (CECCC). IEEE, 2024. https://doi.org/10.1109/ceccc62598.2024.11063507.

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Hayden, Thomas, Joseph Mazzella, Len Krissa, and Paul Murray. "How Not to Do Machine Learning for Corrosion Prediction." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-19310.

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Abstract Machine learning as a tool for automation has grown exponentially in the past two decades. Growth has come from innovations in hardware, such as powerful graphics processing units (GPUs) and cloud computing. Along with hardware advances, there has been an explosion in software packages, algorithms, and tools for performing machine learning. This innovation has resulted in a landscape full of vendor offerings with bold claims about accuracy and precision while not offering much subject matter expertise. The corrosion industry is no exception and faces unique risks in evaluating machine
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Zhu, Jiafan, Xiao Zhang, Konstantinos Menychtas, et al. "GASS: GPU Automated Sharing at Scale." In 2024 IEEE 17th International Conference on Cloud Computing (CLOUD). IEEE, 2024. http://dx.doi.org/10.1109/cloud62652.2024.00056.

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Mohan, Apoorve, Mengmei Ye, Hubertus Franke, Mudhakar Srivatsa, Zhuoran Liu, and Nelson Mimura Gonzalez. "Securing AI Inference in the Cloud: Is CPU-GPU Confidential Computing Ready?" In 2024 IEEE 17th International Conference on Cloud Computing (CLOUD). IEEE, 2024. http://dx.doi.org/10.1109/cloud62652.2024.00028.

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Vulcan, Alexandru mihai, Radu nicolae Pietraru, and Maximilian Nicolae. "VISUAL TOOL FOR LEARNING GPU PROGRAMMING." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-057.

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Graphic Processing Units (GPUs) are unanimously considered as powerful computational resources. General-purpose computing on GPU (GPGPU), as well, is the de facto infrastructure for most of the today computationally intensive problems that researchers all over the globe dill with. High Performance Computing (HPC) facilities use state of the art GPUs. Many domains like deep learning, machine learning, and computational finance uses GPU's for decreasing the execution time. GPUs are widely used in data centers for high performance computing where virtualization techniques are intended for optimiz
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Reports on the topic "Cloud GPUs"

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Ennasr, Osama, Michael Paquette, and Garry Glaspell. UGV SLAM payload for low-visibility environments. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47589.

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Herein, we explore using a low size, weight, power, and cost unmanned ground vehicle payload designed specifically for low-visibility environments. The proposed payload simultaneously localizes and maps in GPS-denied environments via waypoint navigation. This solution utilizes a diverse sensor payload that includes wheel encoders, inertial measurement unit, 3D lidar, 3D ultrasonic sensors, and thermal cameras. Furthermore, the resulting 3D point cloud was compared against a survey-grade lidar.
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