Bibliografías temáticas / Optimisations for GPU

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Literatura académica sobre el tema "Optimisations for GPU"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 19 de julio de 2025

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Artículos de revistas sobre el tema "Optimisations for GPU"

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Amadio, G., J. Apostolakis, P. Buncic, et al. "Offloading electromagnetic shower transport to GPUs." Journal of Physics: Conference Series 2438, no. 1 (2023): 012055. http://dx.doi.org/10.1088/1742-6596/2438/1/012055.

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Abstract Making general particle transport simulation for high-energy physics (HEP) single-instruction-multiple-thread (SIMT) friendly, to take advantage of accelerator hardware, is an important alternative for boosting the throughput of simulation applications. To date, this challenge is not yet resolved, due to difficulties in mapping the complexity of Geant4 components and workflow to the massive parallelism features exposed by graphics processing units (GPU). The AdePT project is one of the R&D initiatives tackling this limitation and exploring GPUs as potential accelerators for offloa
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Yao, Shujun, Shuo Zhang, and Wanhua Guo. "Electromagnetic transient parallel simulation optimisation based on GPU." Journal of Engineering 2019, no. 16 (2019): 1737–42. http://dx.doi.org/10.1049/joe.2018.8587.

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Ebrahim, Abdulla, Andrea Bocci, Wael Elmedany, and Hesham Al-Ammal. "Optimising the Configuration of the CMS GPU Reconstruction." EPJ Web of Conferences 295 (2024): 11015. http://dx.doi.org/10.1051/epjconf/202429511015.

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Particle track reconstruction for high energy physics experiments like CMS is computationally demanding but can benefit from GPU acceleration if properly tuned. This work develops an autotuning framework to automatically optimise the throughput of GPU-accelerated CUDA kernels in CMSSW. The proposed system navigates the complex parameter space by generating configurations, benchmarking performance, and leveraging multi-fidelity optimisation from simplified applications. The autotuned launch parameters improved CMSSW tracking throughput over the default settings by finding optimised, GPU-specifi
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Quan, H., Z. Cui, R. Wang, and Zongjie Cao. "GPU parallel implementation and optimisation of SAR target recognition method." Journal of Engineering 2019, no. 21 (2019): 8129–33. http://dx.doi.org/10.1049/joe.2019.0669.

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Träff, Erik A., Anton Rydahl, Sven Karlsson, Ole Sigmund, and Niels Aage. "Simple and efficient GPU accelerated topology optimisation: Codes and applications." Computer Methods in Applied Mechanics and Engineering 410 (May 2023): 116043. http://dx.doi.org/10.1016/j.cma.2023.116043.

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Szénási, Sándor. "Solving the inverse heat conduction problem using NVLink capable Power architecture." PeerJ Computer Science 3 (November 20, 2017): e138. http://dx.doi.org/10.7717/peerj-cs.138.

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The accurate knowledge of Heat Transfer Coefficients is essential for the design of precise heat transfer operations. The determination of these values requires Inverse Heat Transfer Calculations, which are usually based on heuristic optimisation techniques, like Genetic Algorithms or Particle Swarm Optimisation. The main bottleneck of these heuristics is the high computational demand of the cost function calculation, which is usually based on heat transfer simulations producing the thermal history of the workpiece at given locations. This Direct Heat Transfer Calculation is a well parallelisa
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Bitam, Salim, NourEddine Djedi, and Maroua Grid. "GPU-based distributed bee swarm optimisation for dynamic vehicle routing problem." International Journal of Ad Hoc and Ubiquitous Computing 31, no. 3 (2019): 155. http://dx.doi.org/10.1504/ijahuc.2019.10022343.

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Khemiri, Randa, Hassan Kibeya, Fatma Ezahra Sayadi, Nejmeddine Bahri, Mohamed Atri, and Nouri Masmoudi. "Optimisation of HEVC motion estimation exploiting SAD and SSD GPU-based implementation." IET Image Processing 12, no. 2 (2018): 243–53. http://dx.doi.org/10.1049/iet-ipr.2017.0474.

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Uchida, Akihiro, Yasuaki Ito, and Koji Nakano. "Accelerating ant colony optimisation for the travelling salesman problem on the GPU." International Journal of Parallel, Emergent and Distributed Systems 29, no. 4 (2013): 401–20. http://dx.doi.org/10.1080/17445760.2013.842568.

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Spalding, Myles, Anthony Walsh, and Trent Aland. "Evaluation of a new GPU-enabled VMAT multi-criteria optimisation plan generation algorithm." Medical Dosimetry 45, no. 4 (2020): 368–73. http://dx.doi.org/10.1016/j.meddos.2020.05.007.

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Tesis sobre el tema "Optimisations for GPU"

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Romera, Thomas. "Adéquation algorithme architecture pour flot optique sur GPU embarqué." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS450.

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Cette thèse porte sur l'optimisation et l'implémentation efficace d'algorithmes d'estimation du mouvement des pixels (flot optique) sur des processeurs graphiques (GPU) embarqués. Deux algorithmes itératifs ont été étudiés : la méthode de Variation Totale - L1 (TV-L1) et la méthode de Horn-Schunck. L’objectif est d’obtenir un traitement temps réel (moins de 40 ms par images) sur des plateformes embarquées à faible consommation énergétique, tout en gardant une résolution image et une qualité d’estimation du flot acceptable pour les applications visées. Différents niveaux de stratégies d'optimis
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Fumero, Alfonso Juan José. "Accelerating interpreted programming languages on GPUs with just-in-time compilation and runtime optimisations." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28718.

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Nowadays, most computer systems are equipped with powerful parallel devices such as Graphics Processing Units (GPUs). They are present in almost every computer system including mobile devices, tablets, desktop computers and servers. These parallel systems have unlocked the possibility for many scientists and companies to process significant amounts of data in shorter time. But the usage of these parallel systems is very challenging due to their programming complexity. The most common programming languages for GPUs, such as OpenCL and CUDA, are created for expert programmers, where developers a
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Hopson, Benjamin Thomas Ken. "Techniques of design optimisation for algorithms implemented in software." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20435.

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The overarching objective of this thesis was to develop tools for parallelising, optimising, and implementing algorithms on parallel architectures, in particular General Purpose Graphics Processors (GPGPUs). Two projects were chosen from different application areas in which GPGPUs are used: a defence application involving image compression, and a modelling application in bioinformatics (computational immunology). Each project had its own specific objectives, as well as supporting the overall research goal. The defence / image compression project was carried out in collaboration with the Jet Pr
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Luong, Thé Van. "Métaheuristiques parallèles sur GPU." Thesis, Lille 1, 2011. http://www.theses.fr/2011LIL10058/document.

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Les problèmes d'optimisation issus du monde réel sont souvent complexes et NP-difficiles. Leur modélisation est en constante évolution en termes de contraintes et d'objectifs, et leur résolution est coûteuse en temps de calcul. Bien que des algorithmes approchés telles que les métaheuristiques (heuristiques génériques) permettent de réduire la complexité de leur résolution, ces méthodes restent insuffisantes pour traiter des problèmes de grande taille. Au cours des dernières décennies, le calcul parallèle s'est révélé comme un moyen incontournable pour faire face à de grandes instances de prob
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Chrétien, Benjamin. "Optimisation semi-infinie sur GPU pour le contrôle corps-complet de robots." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT315/document.

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Un robot humanoïde est un système complexe doté de nombreux degrés de liberté, et dont le comportement est sujet aux équations non linéaires du mouvement. Par conséquent, la planification de mouvement pour un tel système est une tâche difficile d'un point de vue calculatoire. Dans ce mémoire, nous avons pour objectif de développer une méthode permettant d'utiliser la puissance de calcul des GPUs dans le contexte de la planification de mouvement corps-complet basée sur de l'optimisation. Nous montrons dans un premier temps les propriétés du problème d'optimisation, et des pistes d'étude pour la
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Van, Luong Thé. "Métaheuristiques parallèles sur GPU." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00638820.

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Les problèmes d'optimisation issus du monde réel sont souvent complexes et NP-difficiles. Leur modélisation est en constante évolution en termes de contraintes et d'objectifs, et leur résolution est coûteuse en temps de calcul. Bien que des algorithmes approchés telles que les métaheuristiques (heuristiques génériques) permettent de réduire la complexité de leur résolution, ces méthodes restent insuffisantes pour traiter des problèmes de grande taille. Au cours des dernières décennies, le calcul parallèle s'est révélé comme un moyen incontournable pour faire face à de grandes instances de prob
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Delevacq, Audrey. "Métaheuristiques pour l'optimisation combinatoire sur processeurs graphiques (GPU)." Thesis, Reims, 2013. http://www.theses.fr/2013REIMS011/document.

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Plusieurs problèmes d'optimisation combinatoire sont dits NP-difficiles et ne peuvent être résolus de façon optimale par des algorithmes exacts. Les métaheuristiques ont prouvé qu'elles pouvaient être efficaces pour résoudre un grand nombre de ces problèmes en leur trouvant des solutions approchées en un temps raisonnable. Cependant, face à des instances de grande taille, elles ont besoin d'un temps de calcul et d'une quantité d'espace mémoire considérables pour être performantes dans l'exploration de l'espace de recherche. Par conséquent, l'intérêt voué à leur déploiement sur des architecture
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Quinto, Michele Arcangelo. "Méthode de reconstruction adaptive en tomographie par rayons X : optimisation sur architectures parallèles de type GPU." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENT109/document.

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La reconstruction tomographique à partir de données de projections est un problème inverse largement utilisé en imagerie médicale et de façon plus modeste pour le contrôle nondestructif. Avec un nombre suffisant de projections, les algorithmes analytiques permettentdes reconstructions rapides et précises. Toutefois, dans le cas d’un faible nombre de vues(imagerie faible dose) et/ou d’angle limité (contraintes spécifiques liées à l’installation), lesdonnées disponibles pour l’inversion ne sont pas complètes, le mauvais conditionnementdu problème s’accentue, et les résultats montrent des artefac
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O'Connell, Jonathan F. "A dynamic programming model to solve optimisation problems using GPUs." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/97930/.

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This thesis presents a parallel, dynamic programming based model which is deployed on the GPU of a system to accelerate the solving of optimisation problems. This is achieved by simultaneously running GPU based computations, and memory transactions, allowing computation to never pause, and overcoming the memory constraints of solving large problem instances. Due to this some optimisation problems, which are currently not solved in an exact manner for real world sized instances due to their complexity, are moved into the solvable realm. The model is implemented to solve, a range of different te
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Pospíchal, Petr. "Akcelerace genetického algoritmu s využitím GPU." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236783.

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This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. First chapter deeply analyses Genetic algorithms and corresponding topics like population, chromosome, crossover, mutation and selection. Next part of the thesis shows GPU abilities for unified computing using both DirectX/OpenGL with Cg and specialized GPGPU libraries like CUDA. The fourth chapter focuses on design of GPU implementation using CUDA, coarse-grained and fine-grained GAs are discussed, and completed by sorting and random number generation task accelerated by GPU. Next chapter covers im
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Capítulos de libros sobre el tema "Optimisations for GPU"

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Nagy, Szilárd, Károly Jármai, and Attila Baksa. "Combination of GPU Programming and FEM Analysis in Structural Optimisation." In Vehicle and Automotive Engineering 4. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15211-5_63.

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Prata, Paula, Paulo Fazendeiro, Pedro Sequeira, and Chandrashekhar Padole. "A Comment on Bio-inspired Optimisation via GPU Architecture: The Genetic Algorithm Workload." In Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_78.

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González-Arribas, Daniel, Manuel Sanjurjo-Rivo, and Manuel Soler. "Multiobjective Optimisation of Aircraft Trajectories Under Wind Uncertainty Using GPU Parallelism and Genetic Algorithms." In Computational Methods in Applied Sciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89890-2_29.

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Porter A.R., Ford R.W., Ashworth M., Riley G.D., and Modani M. "Towards Compiler-Agnostic Performance in Finite-Difference Codes." In Advances in Parallel Computing. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-621-7-647.

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In this paper we evaluate the performance implications of applying a technique which we call PSyKAl to finite difference Ocean models. In PSyKAl the code related to the underlying science is formally separated from code related to parallelisation and single core optimisations. This separation of concerns allows scientists to code their science independently of the underlying hardware architecture (thereby keeping a single code base) and for optimisation specialists to be able to tailor the code for a particular machine independently of the science code. A finite difference shallow water benchmark optimised for cache-based architectures is taken as the starting point. A vanilla PSyKAl version is written and the performance of the two compared. The optimisations that were applied to the original benchmark (loop fusion etc.) are then manually applied to the PSyKAl version as a set of code modifications to the optimisation layer. Performance results are presented for the Cray, Intel and GNU compilers on Intel Ivybridge and Haswell processors and for the IBM compiler on Power8. Results show that the combined set of code modifications obtain performance that is within a few percent of the original code for all compiler and architecture combinations on all tested problem sizes. The only exception to this (other than where we see performance improvement) is the Gnu compiler on Haswell for one problem size. Our tests indicate that this may be due to immature support for that architecture in the Gnu compiler – no such problem is seen on the Ivy Bridge system. Further, the original code performed poorly using the IBM compiler on Power8 and needed to be modified to obtain performant code. Therefore, the PSyKAl approach can be used with negligible performance loss and sometimes small performance gains compared to the original optimised code. We also find that there is no single best hand-optimised implementation of the code for all of the compilers tested.
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Blum Troels and Vinter Brian. "Code Specialisation of Auto-Generated GPU Kernels." In Concurrent Systems Engineering Series. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-886-0-275.

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In this work we explore and evaluate the effect of automatic code specialisation on auto-generated GPU kernels. When combining the high productivity coding environment of computational science with the Just-In-Time compilation nature of many GPU runtime systems there is a clear cut opportunity for code optimisation and specialisation. We have developed a hybrid kernel generation method which is shown to be useful and competitive across very different use cases, and requires minimal knowledge of the overall structure of the program. Stencil codes which are commonly found at the core of computer simulations are ideal candidates for this type of code specialisation. For exactly this type of application we are able to achieve speedups of up to 2.5 times with the implemented strategy.
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Bistaffa Filippo, Bombieri Nicola, and Farinelli Alessandro. "CUBE: A CUDA Approach for Bucket Elimination on GPUs." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-672-9-125.

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We consider Bucket Elimination (BE), a popular algorithmic framework to solve Constraint Optimisation Problems (COPs). We focus on the parallelisation of the most computationally intensive operations of BE, i.e., join sum and maximisation, which are key ingredients in several close variants of the BE framework (including Belief Propagation on Junction Trees and Distributed COP techniques such as ActionGDL and DPOP). In particular, we propose CUBE, a highly-parallel GPU implementation of such operations, which adopts an efficient memory layout allowing all threads to independently locate their input and output addresses in memory, hence achieving a high computational throughput. We compare CUBE with the most recent GPU implementation of BE. Our results show that CUBE achieves significant speed-ups (up to two orders of magnitude) w.r.t. the counterpart approach, showing a dramatic decrease of the runtime w.r.t. the serial version (i.e., up to 652× faster). More important, such speed-ups increase when the complexity of the problem grows, showing that CUBE correctly exploits the additional degree of parallelism inherent in the problem.
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Youcef, Bouras. "Research Information." In Advanced Deep Learning Applications in Big Data Analytics. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2791-7.ch011.

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This chapter describes the framework of an analytical study around the computational intelligence algorithms, which are prompted by natural mechanisms and complex biological phenomena. These algorithms are numerous and can be classified in two great families: firstly the family of evolutionary algorithms (EA) such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategy (ES), differential evolutionary (DE), paddy field algorithm (PFA); secondly, the swarm intelligence algorithms (SIA) such as particle swarm optimisation (PSO), ant colony optimization (ACO), bacteria foraging optimisation (BFO), wolf colony algorithm (WCA), fireworks algorithm (FA), bat algorithm (BA), cockroaches algorithm (CA), social spiders algorithm (SSA), cuckoo search algorithm (CSA), wasp swarm optimisation (WSO), mosquito optimisation algorithm (MOA). The authors have detailed the functioning of each algorithm following a structured organization (the descent of the algorithm, the inspiration source, the summary, and the general process) that offers for readers a thorough understanding. This study is the fruit of many years of research in the form of synthesis, which groups the contributions offered by several researchers in the meta-heuristic field. It can be the beginning point for planning and modelling new algorithms or improving existing algorithms.
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Orlandi Andrea, Benedetti Riccardo, Capecchi Valerio, Ortolani Alberto, and Costalli Luigi. "Preliminary Assessment of Route Optimisation for Fuel Minimisation and Safety of Navigation by the Use of Cooperatively Collected Data at Sea." In Technology and Science for the Ships of the Future. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-870-9-462.

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The growing pressure of the international regulations on GHG emissions from ships is pushing towards the adoption of a variety of operational energy efficiency measures. The fusion of measurement techniques, smart telecommunication technologies and numerical modelling approaches has a great potential for the implementation of services for the shipping industry. Among these, there are weather routing systems for improving both energy efficiency and navigational safety. PROFUMO Demonstrator is an ESA ARTES Integrated Applications Promotion (IAP) Programme project. Its main goal is to implement a pre-operational system for fleet management and weather routing services, based on the cooperative collection of meteo-marine data from ships, to improve weather forecasts. Atmospheric information from GNSS signals (namely Galileo and GPS) are also utilised to improve numerical weather predictions and enable detailed route optimisation services at the Mediterranean scale. The architecture of the system and some first implementation results will be described, in particular on the integration of meteo-marine forecasting with ship modelling and route optimisation, with some sensitivity analyses for the optimisation process, under different approaches on modelling wind and waves added resistances for computing the ship powering performance. In perspective we imagine the use of in-service measured data to dynamically improve the ship modelling components of the system.
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Soares, Adroaldo Santos, Lilian Lefol Nani Guarieiro, Oberdan Rocha Pinheiro, Marcelo Albano Moret Simões Gonçalves, Fabio de Sousa Santos, and Fernando Luiz Pellegrini Pessoa. "Metamodeling of the deposition process in oil pre-processing to optimise the cleaning of the heat exchanger network: A systematic review." In Themes focused on interdisciplinarity and sustainable development worldwide V. 02. Seven Editora, 2024. http://dx.doi.org/10.56238/sevened2024.003-009.

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Identifying and analysing possible metamodelling techniques to optimise the performance of heat exchangers in oil pre-processing from the point of view of the deposition process is of great importance for evaluating the performance of heat exchangers in different operating and maintenance configurations in order to increase their energy efficiency, since during the operation of heat exchanger networks, deposition on the heat exchange surfaces is common, reducing their effectiveness. In this article, a systematic review was carried out to study the metamodelling techniques and optimisation tools used. The results of the study showed that there are some techniques used such as: Recurrent Neural Networks (RNN); Multi-Layer Perceptron (MPL); Long Short-Term Memory (LSTM); Gated Recurrent Unit (GRU); Recurrent Convolutional Neural Network (RCNN), and tools that will be covered in this study.
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Zanella Riccardo, Fiameni Giuseppe, and Rorro Marco. "A Performance Study of Machine and Deep Learning Frameworks on Cineca HPC Systems." In Advances in Parallel Computing. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-843-3-550.

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With the data doubling every year, data intensive applications are increasing as well as the demand of high-end resource capacity to analyse collected data sets. The explosion of analysis applications have become a major driver for revising system architecture and tools leading to the proliferation of software components and frameworks which may require multi-node and multi-core systems to scale-up and provide good performance. In this context, Machine learning and Deep learning are steadily proving to be successful methods for a variety of use cases, and their popularity has resulted in numerous open-source software tools becoming accessible to the public and popular across different scientific disciplines. But with the growth of applications and tools, it is becoming difficult for researchers to estimate how much resource is needed to run their analyses and select appropriate software and hardware components. The goal of this paper is to present the results of a preliminary comparative study of state-of-the-art machine and deep learning tools and benchmark them on Cineca HPC systems. The comparison has been done taking in consideration different factors including the impossibility to benchmark all tools available on the market, the existence of tools supporting hardware accelerators, such as GPU, and the availability of precedent studies [1,2]. Our preliminary results show that tested tools are able to leverage underneath system capabilities to achieve significant performance and that no single software exists that outperforms others opening space to further optimisation.
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Actas de conferencias sobre el tema "Optimisations for GPU"

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Lemos, Dayllon V. X., Humberto J. Longo, Wellington S. Martins, and Les R. Foulds. "A GPU-based DP algorithm for solving multiple instances of the knapsack problem." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/wscad.2023.235875.

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The knapsack problem is a classic and fundamental optimisation problem that serves as a subproblem in various optimisation algorithms. Thus, it is of great importance that we manage to solve several instances of the knapsack problem in a fast and efficient way. In this work we present a parallel algorithm, based on dynamic programming, that can take advantage of parallelism as more knapsacks need to be solved. The algorithm makes use of fine-grained data parallelism and is easily mapped to GPU accelerators. Extensive experiments with diverse datasets demonstrate the superiority of the proposed
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Paukste, Andrius. "Monte Carlo optimisation auto-tuning on a multi-GPU cluster." In 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2012. http://dx.doi.org/10.1109/pdgc.2012.6449942.

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Wainwright, Thomas R., Daniel J. Poole, and Christian B. Allen. "GPU-accelerated aerodynamic shape optimisation framework for large turbine blades." In AIAA SCITECH 2022 Forum. American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-1292.

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Maknickienė, Nijolė, Ieva Kekytė, and Algirdas Maknickas. "COMPUTATION INTELLIGENCE BASED DAILY ALGORITHMIC STRATEGIES FOR TRADING IN THE FOREIGN EXCHANGE MARKET." In Business and Management 2018. VGTU Technika, 2018. http://dx.doi.org/10.3846/bm.2018.53.

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Successful trading in financial markets is not possible without a support system that manages the preparation of the data, prediction system, and risk management and evaluates the trading efficien-cy. Selected orthogonal data was used to predict exchange rates by applying recurrent neural network (RNN) software based on the open source framework Keras and the graphical processing unit (GPU) NVIDIA GTX1070 to accelerate RNN learning. The newly developed software on the GPU predicted ten high-low distributions in approximately 90 minutes. This paper compares different daily algorith-mic trading
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Jaros, Jiri, Jan Marek, and Pavel Mensik. "Optimisation of Water Management Systems Using a GPU-Accelerated Differential Evolution." In 2015 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2015. http://dx.doi.org/10.1109/ssci.2015.266.

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Li, Da, Hancheng Wu, and Michela Becchi. "Exploiting Dynamic Parallelism to Efficiently Support Irregular Nested Loops on GPUs." In COSMIC '15: International Workshop on Code Optimisation for Multi and Many Cores. ACM, 2015. http://dx.doi.org/10.1145/2723772.2723780.

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Thomas, B., A. El Ouardi, S. Bouaziz, R. Le Goff Latimier, and H. Ben Ahmed. "GPU Optimisation of an Endogenous Peer-to-Peer Market with Product Differentiation." In 2023 IEEE Belgrade PowerTech. IEEE, 2023. http://dx.doi.org/10.1109/powertech55446.2023.10202823.

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Lu, Xin, Guoqing Han, Peng Dong, Luting Wang, Zhuangzhuang Zhang, and Xingyuan Liang. "Energy Consumption Prediction and Optimization of Electrical Submersible Pump Well System Based on DA-RNN Algorithm." In SPE Symposium and Exhibition - Production Enhancement and Cost Optimisation. SPE, 2024. http://dx.doi.org/10.2118/220625-ms.

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Abstract Electrical submersible pump(ESP) well system is widely used in the oil industry due to its advantages of high displacement and lift capability. However, it is associated with significant energy consumption. In order to conserve electrical energy and enhance the efficiency of petroleum companies, a deep learning-based energy consumption calculation method is proposed and utilized to optimize the most energy-efficient operating regime. The energy consumption of the ESP well system is precisely determined through the application of the Pearson correlation coefficient analysis method, whi
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Cecilia, J. M., J. M. Garcia, M. Ujaldon, A. Nisbet, and M. Amos. "Parallelization strategies for ant colony optimisation on GPUs." In Distributed Processing, Workshops and Phd Forum. IEEE, 2011. http://dx.doi.org/10.1109/ipdps.2011.170.

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Dybedal, Joacim, and Geir Hovland. "GPU-Based Optimisation of 3D Sensor Placement Considering Redundancy, Range and Field of View." In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea48937.2020.9248170.

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