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Cloud application platform - Virtualization vs Containerization : A comparison between application containers and virtual machinesVestman, Simon January 2017 (has links)
Context. As the number of organizations using cloud application platforms to host their applications increases, the priority of distributing physical resources within those platforms is increasing simultaneously. The goal is to host a higher quantity of applications per physical server, while at the same time retain a satisfying rate of performance combined with certain scalability. The modern needs of customers occasionally also imply an assurance of certain privacy for their applications. Objectives. In this study two types of instances for hosting applications in cloud application platforms, virtual machines and application containers, are comparatively analyzed. This investigation has the goal to expose advantages and disadvantages between the instances in order to determine which is more appropriate for being used in cloud application platforms, in terms of performance, scalability and user isolation. Methods. The comparison is done on a server running Linux Ubuntu 16.04. The virtual machine is created using Devstack, a development environment of Openstack, while the application container is hosted by Docker. Each instance is running an apache web server for handling HTTP requests. The comparison is done by using different benchmark tools for different key usage scenarios and simultaneously observing the resource usage in respective instance. Results. The results are produced by investigating the user isolation and resource occupation of respective instance, by examining the file system, active process handling and resource allocation after creation. Benchmark tools are executed locally on respective instance, for a performance comparison of the usage of physical resources. The amount of CPU operations executed within a given time is measured in order determine the processor performance, while the speed of read and write operations to the main memory is measured in order to determine the RAM performance. A file is also transmitted between host server and application in order to compare the network performance between respective instance, by examining the transfer speed of the file. Lastly a set of benchmark tools are executed on the host server to measure the HTTP server request handling performance and scalability of each instance. The amount of requests handled per second is observed, but also the resource usage for the request handling at an increasing rate of served requests and clients. Conclusions. The virtual machine is a better choice for applications where privacy is a higher priority, due to the complete isolation and abstraction from the rest of the physical server. Virtual machines perform better in handling a higher quantity of requests per second, while application containers is faster in transferring files through network. The container requires a significantly lower amount of resources than the virtual machine in order to run and execute tasks, such as responding to HTTP requests. When it comes to scalability the prefered type of instance depends on the priority of key usage scenarios. Virtual machines have quicker response time for HTTP requests but application containers occupy less physical resources, which makes it logically possible to run a higher quantity of containers than virtual machines simultaneously on the same physical server.
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The Thermal-Constrained Real-Time Systems Design on Multi-Core Platforms -- An Analytical ApproachSHA, SHI 21 March 2018 (has links)
Over the past decades, the shrinking transistor size enabled more transistors to be integrated into an IC chip, to achieve higher and higher computing performances. However, the semiconductor industry is now reaching a saturation point of Moore’s Law largely due to soaring power consumption and heat dissipation, among other factors. High chip temperature not only significantly increases packing/cooling cost, degrades system performance and reliability, but also increases the energy consumption and even damages the chip permanently. Although designing 2D and even 3D multi-core processors helps to lower the power/thermal barrier for single-core architectures by exploring the thread/process level parallelism, the higher power density and longer heat removal path has made the thermal problem substantially more challenging, surpassing the heat dissipation capability of traditional cooling mechanisms such as cooling fan, heat sink, heat spread, etc., in the design of new generations of computing systems. As a result, dynamic thermal management (DTM), i.e. to control the thermal behavior by dynamically varying computing performance and workload allocation on an IC chip, has been well-recognized as an effective strategy to deal with the thermal challenges.
Over the past decades, the shrinking transistor size, benefited from the advancement of IC technology, enabled more transistors to be integrated into an IC chip, to achieve higher and higher computing performances. However, the semiconductor industry is now reaching a saturation point of Moore’s Law largely due to soaring power consumption and heat dissipation, among other factors. High chip temperature not only significantly increases packing/cooling cost, degrades system performance and reliability, but also increases the energy consumption and even damages the chip permanently. Although designing 2D and even 3D multi-core processors helps to lower the power/thermal barrier for single-core architectures by exploring the thread/process level parallelism, the higher power density and longer heat removal path has made the thermal problem substantially more challenging, surpassing the heat dissipation capability of traditional cooling mechanisms such as cooling fan, heat sink, heat spread, etc., in the design of new generations of computing systems. As a result, dynamic thermal management (DTM), i.e. to control the thermal behavior by dynamically varying computing performance and workload allocation on an IC chip, has been well-recognized as an effective strategy to deal with the thermal challenges.
Different from many existing DTM heuristics that are based on simple intuitions, we seek to address the thermal problems through a rigorous analytical approach, to achieve the high predictability requirement in real-time system design. In this regard, we have made a number of important contributions. First, we develop a series of lemmas and theorems that are general enough to uncover the fundamental principles and characteristics with regard to the thermal model, peak temperature identification and peak temperature reduction, which are key to thermal-constrained real-time computer system design. Second, we develop a design-time frequency and voltage oscillating approach on multi-core platforms, which can greatly enhance the system throughput and its service capacity. Third, different from the traditional workload balancing approach, we develop a thermal-balancing approach that can substantially improve the energy efficiency and task partitioning feasibility, especially when the system utilization is high or with a tight temperature constraint. The significance of our research is that, not only can our proposed algorithms on throughput maximization and energy conservation outperform existing work significantly as demonstrated in our extensive experimental results, the theoretical results in our research are very general and can greatly benefit other thermal-related research.
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DÉVELOPPEMENT D'UNE MÉTHODE IMPLICITE SANS MATRICE POUR LA SIMULATION 2D-3D DES ÉCOULEMENTS COMPRESSIBLES ET FAIBLEMENT COMPRESSIBLES EN MAILLAGES NON-STRUCTURÉSKloczko, Thibaud 15 March 2006 (has links) (PDF)
Les calculs d'écoulements stationnaires peuvent être considérés comme efficace si l?état stationnaire est atteint pour un temps CPU réduit mais aussi si la place mémoire utilisée reste faible; cette dernière exigence devient primordiale pour les applications industrielles où le nombre de points de calcul est très important. Ceci vaut également pour les écoulements instationnaires, désormais classiquement résolus via une approche pas-de-temps dual pour laquelle les états physiques successifs sont vus comme des états stationnaires vis-à-vis d'un temps fictif. Le besoin crucial de méthodes implicites à faible encombrement mémoire a conduit au développement de traitements sans matrice. Pour les applications qui intéressent le CEA, à savoir la simulation d'écoulements réactifs multi-espèces à l'intérieur d'une enceinte de réacteur nucléaire à eau pressurisée, les méthodes doivent être assez versatiles pour traiter la gamme d'écoulements allant du quasi-incompressible au fortement compressible. Le préconditionnement bas-Mach des équations de Navier-Stokes permet d'appliquer en régime incompressible les schémas initialement conçus pour la simulation des écoulements compressibles. Le présent travail montre comment obtenir un traitement implicite sans matrice pour tout régime d'écoulement lorsque la phase implicite contient une matrice de préconditionnement; l'efficacité intrinsèque du schéma implicite sans matrice couplé à une technique de relaxation de type Jacobi par point (PJ) ou Symmetric Gauss-Seidel (SGS) est étudiée grâce à une analyse de Von Neumann; puis des comparaisons avec des méthodes implicites blocs standards sont effectuées. La méthode implicite sans matrice est finalement implémentée au sein du code non-structuré CAST3M et elle est appliquée à la modélisation d'un Té de mélange à faible nombre de Mach. Le schéma implicite sans matrice constitue une alternative compétitive pour la simulation des écoulements compressibles et faiblement compressibles en maillages non-structurés.
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Tuned and asynchronous stencil kernels for CPU/GPU systemsVenkatasubramanian, Sundaresan 18 May 2009 (has links)
We describe heterogeneous multi-CPU and multi-GPU implementations of Jacobi's iterative method for the 2-D Poisson equation on a structured grid, in both single- and double-precision. Properly tuned, our best implementation achieves 98% of the empirical streaming GPU bandwidth (66% of peak) on a NVIDIA C1060. Motivated to find a still faster implementation, we further consider "wildly asynchronous" implementations that can reduce or even eliminate the synchronization bottleneck between iterations. In these versions, which are based on the principle of a chaotic relaxation (Chazan and Miranker, 1969), we simply remove or delay synchronization between iterations, thereby potentially trading off more flops (via more iterations to converge) for a higher degree of asynchronous parallelism. Our relaxed-synchronization implementations on a GPU can be 1.2-2.5x faster than our best synchronized GPU implementation while achieving the same accuracy. Looking forward, this result suggests research on similarly "fast-and-loose" algorithms in the coming era of increasingly massive concurrency and relatively high synchronization or communication costs.
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Finite Element Approximations of 2D Incompressible Navier-Stokes Equations Using Residual ViscositySjösten, William, Vadling, Victor January 2018 (has links)
Chorin’s method, Incremental Pressure Correction Scheme (IPCS) and Crank-Nicolson’s method (CN) are three numerical methods that were investigated in this study. These methods were here used for solving the incompressible Navier-Stokes equations, which describe the motion of an incompressible fluid, in three different benchmark problems. The methods were stabilized using residual based artificial viscosity, which was introduced to avoid instability. The methods were compared in terms of accuracy and computational time. Furthermore, a theoretical study of adaptivity was made, based on an a posteriori error estimate and an adjoint problem. The implementation of the adaptivity is left for future studies. In this study we consider the following three well-known benchmark problems: laminar 2D flow around a cylinder, Taylor-Green vortex and lid-driven cavity problem. The difference of the computational time for the three methods were in general relatively small and differed depending on which problem that was investigated. Furthermore the accuracy of the methods also differed in the benchmark problems, but in general Crank-Nicolson’s method gave less accurate results. Moreover the stabilization technique worked well when the kinematic viscosity of the fluid was relatively low, since it managed to stabilize the numerical methods. In general the solution was affected in a negative way when the problem could be solved without stabilization for higher viscosities.
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Mitteilungen des URZ 2/2002Heik,, Wegener,, Ziegler,, Jehmlich,, Krause,, Horbach,, Riedel,, Hübsch,, Wegener, J., Dippmann,, Brose,, Heide,, Fischer, 30 August 2002 (has links)
Mitteilungen des URZ 2/2002
(Inhalt siehe Schlagwörter)
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Valorisation d’options américaines et Value At Risk de portefeuille sur cluster de GPUs/CPUs hétérogène / American option pricing and computation of the portfolio Value at risk on heterogeneous GPU-CPU clusterBenguigui, Michaël 27 August 2015 (has links)
Le travail de recherche décrit dans cette thèse a pour objectif d'accélérer le temps de calcul pour valoriser des instruments financiers complexes, tels des options américaines sur panier de taille réaliste (par exemple de 40 sousjacents), en tirant partie de la puissance de calcul parallèle qu'offrent les accélérateurs graphiques (Graphics Processing Units). Dans ce but, nous partons d'un travail précédent, qui avait distribué l'algorithme de valorisation de J.Picazo, basé sur des simulations de Monte Carlo et l'apprentissage automatique. Nous en proposons une adaptation pour GPU, nous permettant de diviser par 2 le temps de calcul de cette précédente version distribuée sur un cluster de 64 cœurs CPU, expérimentée pour valoriser une option américaine sur 40 actifs. Cependant, le pricing de cette option de taille réaliste nécessite quelques heures de calcul. Nous étendons donc ce premier résultat dans le but de cibler un cluster de calculateurs, hétérogènes, mixant GPUs et CPUs, via OpenCL. Ainsi, nous accélérons fortement le temps de valorisation, même si les entrainements des différentes méthodes de classification expérimentées (AdaBoost, SVM) sont centralisés et constituent donc un point de blocage. Pour y remédier, nous évaluons alors l'utilisation d'une méthode de classification distribuée, basée sur l'utilisation de forêts aléatoires, rendant ainsi notre approche extensible. La dernière partie réutilise ces deux contributions dans le cas de calcul de la Value at Risk d’un portefeuille d'options, sur cluster hybride hétérogène. / The research work described in this thesis aims at speeding up the pricing of complex financial instruments, like an American option on a realistic size basket of assets (e.g. 40) by leveraging the parallel processing power of Graphics Processing Units. To this aim, we start from a previous research work that distributed the pricing algorithm based on Monte Carlo simulation and machine learning proposed by J. Picazo. We propose an adaptation of this distributed algorithm to take advantage of a single GPU. This allows us to get performances using one single GPU comparable to those measured using a 64 cores cluster for pricing a 40-assets basket American option. Still, on this realistic-size option, the pricing requires a handful of hours. Then we extend this first contribution in order to tackle a cluster of heterogeneous devices, both GPUs and CPUs programmed in OpenCL, at once. Doing this, we are able to drastically accelerate the option pricing time, even if the various classification methods we experiment with (AdaBoost, SVM) constitute a performance bottleneck. So, we consider instead an alternate, distributable approach, based upon Random Forests which allow our approach to become more scalable. The last part reuses these two contributions to tackle the Value at Risk evaluation of a complete portfolio of financial instruments, on a heterogeneous cluster of GPUs and CPUs.
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Realizace superpočítače pomocí grafické karty / Realization of supercomputer using graphic cardJasovský, Filip January 2014 (has links)
This master´s thesis deals with realization of supercomputer using graphic card with CUDA technology. The theoretical part of this thesis describes the function and the possibility of graphic cards and desktop computers and processes taking place in the proces sof calculations on them. The practical part deals with creation system for calculations on the graphic card using the algorithm of artificial intelligence, more specifically artificial neural networks. Subsequently is the generated program used for data classification of large input data file. Finally the results are compared.
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Mitteilungen des URZ 2/2002Heik, Wegener, Ziegler, Jehmlich, Krause, Horbach, Riedel, Hübsch, Wegener, J., Dippmann, Brose, Heide, Fischer 30 August 2002 (has links)
Mitteilungen des URZ 2/2002
(Inhalt siehe Schlagwörter)
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Investigation of hierarchical deep neural network structure for facial expression recognitionMotembe, Dodi 01 1900 (has links)
Facial expression recognition (FER) is still a challenging concept, and machines struggle to
comprehend effectively the dynamic shifts in facial expressions of human emotions. The
existing systems, which have proven to be effective, consist of deeper network structures that
need powerful and expensive hardware. The deeper the network is, the longer the training and
the testing. Many systems use expensive GPUs to make the process faster. To remedy the
above challenges while maintaining the main goal of improving the accuracy rate of the
recognition, we create a generic hierarchical structure with variable settings. This generic
structure has a hierarchy of three convolutional blocks, two dropout blocks and one fully
connected block. From this generic structure we derived four different network structures to
be investigated according to their performances. From each network structure case, we again
derived six network structures in relation to the variable parameters. The variable parameters
under analysis are the size of the filters of the convolutional maps and the max-pooling as
well as the number of convolutional maps. In total, we have 24 network structures to
investigate, and six network structures per case. After simulations, the results achieved after
many repeated experiments showed in the group of case 1; case 1a emerged as the top
performer of that group, and case 2a, case 3c and case 4c outperformed others in their
respective groups. The comparison of the winners of the 4 groups indicates that case 2a is the
optimal structure with optimal parameters; case 2a network structure outperformed other
group winners. Considerations were done when choosing the best network structure,
considerations were; minimum accuracy, average accuracy and maximum accuracy after 15
times of repeated training and analysis of results. All 24 proposed network structures were
tested using two of the most used FER datasets, the CK+ and the JAFFE. After repeated
simulations the results demonstrate that our inexpensive optimal network architecture
achieved 98.11 % accuracy using the CK+ dataset. We also tested our optimal network
architecture with the JAFFE dataset, the experimental results show 84.38 % by using just a
standard CPU and easier procedures. We also compared the four group winners with other
existing FER models performances recorded recently in two studies. These FER models used
the same two datasets, the CK+ and the JAFFE. Three of our four group winners (case 1a,
case 2a and case 4c) recorded only 1.22 % less than the accuracy of the top performer model
when using the CK+ dataset, and two of our network structures, case 2a and case 3c came in
third, beating other models when using the JAFFE dataset. / Electrical and Mining Engineering
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