GPU clusters for high-performance computing

Kindratenko, Volodymyr; Enos, Jeremy; Shi, Guosheng; Showerman, Michael; Arnold, Galen Wesley; Stone, John E.; Phillips, J. C.; Hwu, Wen-mei W.

doi:10.1109/clustr.2009.5289128

Cited by 168 publications

(85 citation statements)

References 8 publications

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“…This allows a computation on several GPUs simultaneously. It will allow us to consider finer meshes and also reduce the computation time [1,11,13].…”

Section: Mpimentioning

confidence: 99%

Two-fluid compressible simulations on GPU cluster

Helluy

Jung

2014

ESAIM: ProcS

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Abstract. In this work we propose an efficient finite volume approximation of two-fluid flows. Our scheme is based on three ingredients. We first construct a conservative scheme that removes the pressure oscillations phenomenon at the interface. The construction relies on a random sampling at the interface [5,6]. Secondly, we replace the exact Riemann solver by a faster relaxation Riemann solver with good stability properties [4]. Finally, we apply Strang directional splitting and optimized memory transpositions in order to achieve high performance on Graphics Processing Unit (GPU) or GPU cluster computations. Résumé. Nous proposons une méthode de volumes finis efficace pour l'approximation d'écoulementsbifluides. Le schémas est basé sur trois ingrédients. Nous construisons d'abord un schéma conservatif sans oscillations de pression. La construction repose sur un échantillonnage aléatoire à l'interface. Ensuite, nous remplaçons le solveur de Riemann exact par un solveur approché de relaxation plus rapide, et qui possède de bonnes propriétés de stabilité. Finalement, nous appliquons un splitting directionnel de Strang et des techniques de transposition optimisées en mémoire pour atteindre de bonnes performances sur GPU ou cluster de GPU.

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“…This allows a computation on several GPUs simultaneously. It will allow us to consider finer meshes and also reduce the computation time [1,11,13].…”

Section: Mpimentioning

confidence: 99%

Two-fluid compressible simulations on GPU cluster

Helluy

Jung

2014

ESAIM: ProcS

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“…Due to its huge parallel hardware architecture and high performance of floating point arithmetic and memory operations on GPUs make them particularly well-suited to many of the same scientific and engineering workloads that occupy HPC (High Performance Computing) clusters, and that is why they are used as HPC accelerators [3]. Apart from being cost-effective such accelerators, GPUs also significantly reduce space, power, and cooling demands, and reduce the number of operating system images that must be managed in comparison to traditional CPU-only clusters of similar aggregate computational capability [4].…”

Section: Graphics Processing Unitmentioning

confidence: 99%

Accelerating CBIR System using Graphics Processing Unit in OPEN CV Environment

Kaur¹,

Jindal²

2015

IJCA

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Content Based Image Retrieval is a system for retrieving the images from a dataset of images. The CBIR has several stages including feature extraction at the initial level and then performing the similarity matching. Later on based upon the similarity matching results the required images can be extracted. Images are a complex data to handle as they are composed of matrices and vectors of data and also due to multi thread execution of algorithms, programmability and low cost, image processing becomes an appropriate field of achieving parallelism. To parallelize the phases of CBIR a special hardware is used known as Graphics Processing Unit. The objectives of the published work is to develop a Content Based Image Retrieval system and test it on benchmark WANG database using suitable and efficient techniques so as to achieve efficient results. As images are complex and there is an approach of achieving parallelism while dealing with images, so after implementing CBIR, the feature extraction phase has been parallelized and the tendency of GPU processor is exploited to obtain the speed up. An accuracy of range between 22 to 67% for individual categories of the databasewas achieved. Finally on comparing the execution times of serial implementation and parallel implementation a considerable speed up of 15 % on an average was obtained.

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“…GPGPU cloud computing [16][17][18][19][20][21]24] is more broadly applicable, offering general purpose computing ability in the manner of on-demand virtual resources. In GPGPU cloud computing, the processing strength of physical resources is divided by virtual resources and summoned obliquely.…”

Section: B Improving Game Engine Performance In Terms Of Physics and Aimentioning

confidence: 99%

Comparison between Famous Game Engines and Eminent Games

Mishra¹,

Shrawankar²

2016

IJIMAI

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-Nowadays game engines are imperative for building 3D applications and games. This is for the reason that the engines appreciably reduce resources for employing obligatory but intricate utilities. This paper elucidates about a game engine, popular games developed by these engines and its foremost elements. It portrays a number of special kinds of contemporary game developed by engines in the way of their aspects, procedure and deliberates their stipulations with comparison.Keywords -Game Engine, Non-Cloud Gaming Platforms, Cloud Gaming Platforms, Popular Games, GPGPU I. IntRoductIon I n general, the notion of game engine is very easy to comprehend. It is a platform for performing game related tasks like interpretation, physics related reckoning, and to facilitate developers for focusing on the niceties that make the game inimitable. Engines are in reality an assemblage of reusable modules that can be manipulated in order to carry a game towards realism. Indeed, there are certain disparities between a game and a game engine [1]. Graphics, animation, audio, physics, UI and AI are the major different constituents of an engine. Conversely the subject matter of a game, its definite characters and background, real world avatar and its behaviours etc. are the components that create the real game. Game engines are middleware's.Game engines produce the replication of actual world in the digital world by controlling the elementary physics. Games developed by these engines make user, casual or die-hard player. In general, smartphone operators, as mobile game players, are alike casual gamers rather than die-hard gamers. In contradiction of die-hard gamers, casual gamers are outlined as less dedicated, less spirited, and more tranquil users. Casual gamers incline to be not as much of ardent and less fascinated to classy or multifarious games (comparative to gameplay, achieve target, environs, graphics, chaps, etc.).The market for video games is growing, with sales in 2015 of $91.5 billion marking an 11.84% increase over 2014, at this price global revenues are expected to reach $107 billion in 2017. However, growth is not only in sales but also in the miscellany of matter offered, vacillating from scholastic games to first-person shooters. In addition, a captivating conjunction of mass media is proceeding with video games, having motion picture eminence cut-scenes and voiceover.In a cloud environment, the task of service provider is separated into two: the infrastructure providers managing cloud platforms and rent assets conferring to a usage, and service providers leasing assets from one or more infrastructure providers to assist the users. Lately, a novel type of cloud service has been familiarized, which have the utmost severe exigencies on network Quality of Service (QoS) to date known as cloud gaming. However, in cloud gaming the complete user experience is provided through the network. This creates dissimilarity between cloud gaming and conventional online Gaming in stipulations of network quality of experience...

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GPU clusters for high-performance computing

Cited by 168 publications

References 8 publications

Two-fluid compressible simulations on GPU cluster

Two-fluid compressible simulations on GPU cluster

Accelerating CBIR System using Graphics Processing Unit in OPEN CV Environment

Comparison between Famous Game Engines and Eminent Games

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