Creating a 3D game engine is not a trivial task as gamers often demand for high quality output with top notch performance in games. In this paper, we show how various real-time rendering algorithms can be applied to i...
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Creating a 3D game engine is not a trivial task as gamers often demand for high quality output with top notch performance in games. In this paper, we show how various real-time rendering algorithms can be applied to implement a practical 3D game engine. We explore the general architecture of a 3D engine and discuss the role of a scene graph in a 3D engine. We look at scene graph from the software engineering perspective. In particular, we show the way to design a scene graph that is object-oriented and portable across different rendering engine. Then, we explain the algorithms that we apply to speed up the performance of our 3D engine. We optimize the 3D engine on the scene graph and object geometry levels. The algorithms that we propose are expected to perform reasonably well for both static and dynamic scenes. Finally, we give a brief preview on the possibility of parallel processing in scene graph to create a 3D engine with multiprocessing capability.
In order to optimally implement real time, high throughput, data intensive multimedia applications, it is crucial to optimize the performance of the memory subsystem to minimize excessive off-chip memory bandwidth sub...
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In order to optimally implement real time, high throughput, data intensive multimedia applications, it is crucial to optimize the performance of the memory subsystem to minimize excessive off-chip memory bandwidth subject to the constraint of available on-chip memory cache size. This can be accomplished by customizing algorithm transformation and designing a customized cache address mapping algorithm for a specific class of multimedia applications. In this paper, we propose an algorithm transformation and customized cache mapping to improve the data reusability and reduce address conflict which in turn, reduces the cache miss and memory I/O bandwidth for the block-based full-search motion estimation algorithm. Simulation results using test video sequences demonstrate marked performance improvement.
Protected working capacity envelope concept has been proposed to simplify network management and operation in survivable WDM networks. Dynamic lightpath provisioning within working capacity envelope is only required t...
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Protected working capacity envelope concept has been proposed to simplify network management and operation in survivable WDM networks. Dynamic lightpath provisioning within working capacity envelope is only required to perform shortest path routing algorithm. Based on PWCE concept, we propose an efficient approximation approach, called Lagrangean relaxation with binary exponential reduction heuristics (LBER), aimed, to resolve the routing and capacity assignment (RCA) problem in survivable WDM networks to protect any single link failure. The task is first formulated as a mixed integer non-linear optimization problem in which the bottleneck link blocking probability for call setup is to be minimized. By sequentially solving a series of Lagrangean relaxation problems, the range of uncertainty of target blocking probability is reduced by half between the solutions of two adjacent problems such that the near optimal solution can be obtained in short time. We conduct a performance study on the proposed algorithms under different parameter settings. We further draw comparisons between LBER and a minimum working hop heuristic approach via experiments over two well known benchmark networks. Numerical results demonstrate that LBER outperforms minimum working hop approach for networks with both uniform and non-uniform demand distributions.
Multiprocessors are about to become prevalent in the PC world. Major CPU vendors such as Intel and Advanced Micro Devices have recently announced their imminent migration to multicore processors. Affine partitioning p...
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Multiprocessors are about to become prevalent in the PC world. Major CPU vendors such as Intel and Advanced Micro Devices have recently announced their imminent migration to multicore processors. Affine partitioning provides a systematic framework to find asymptotically optimal computation and data decomposition for multiprocessors, including multicore processors. This affine framework uniformly models a large class of high-level optimizations such as loop interchange, reversal, skewing, fusion, fission, re-indexing, scaling, and statement reordering. However, the resulting code after applying affine transformations tends to contain more loop levels and complex conditional expressions. This impacts performance, code readability and debuggability for both programmers and compiler developers. To facilitate the adoption of affine partitioning in industry, we address the above practical issues by proposing a salient two-step algorithm: coalesce and optimize. The coalescing algorithm maintains valid code throughout and improves readability and debuggability. We demonstrate with examples that the optimization algorithm simplifies the resulting loop structures, conditional expressions and array access functions and generates efficient code
In this paper, we propose a nonmonotone trust-region algorithm for the solution of optimization problems with general nonlinear equality constraints and simple bounds. Under a constant rank assumption on the gradients...
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In this paper, we propose a nonmonotone trust-region algorithm for the solution of optimization problems with general nonlinear equality constraints and simple bounds. Under a constant rank assumption on the gradients of the active constraints, we analyze the global convergence of the proposed algorithm.
nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger the dependence on efficient and scalable software is severe. Toolkit fo...
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ISBN:
(纸本)076952138X
nonlinear conjugate gradient methods (CG) are typical unconstrained optimization methods. As the optimization problems to be solved become larger the dependence on efficient and scalable software is severe. Toolkit for Advanced optimization (TAO) is a parallel package, that can currently solve several kinds of optimization problems. In this paper we give the framework of several variants of CG: CG_FR, CG_PR, CG_PRP and their implementations in TAO1.5, which have been tested up to 64 processors on Dawning2000 to solve problems with up to 10(6) variables. The results show that the scalability of CG implementations in TAO1.5 is excellent.
This paper introduces GENOSIM-p: a Generic traffic microsimulation parameter optimization tool using Parallel Genetic algorithms (PGA), and its implementation to the St. Clair network in Downtown Toronto, Canada. GENO...
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ISBN:
(纸本)0889864241
This paper introduces GENOSIM-p: a Generic traffic microsimulation parameter optimization tool using Parallel Genetic algorithms (PGA), and its implementation to the St. Clair network in Downtown Toronto, Canada. GENOSIM-p is the parallel version of previous optimizationsoftware GENOSIM [1]. GENOSIM-p employs PGA to calibrate traffic microsimulation models. In this research, we will use PARAMICS: a microscopic traffic simulation platform. PARAMICS consists of highperformance cross-linked traffic models having multiple user-adjustable parameters. GENOSIM-p will use PGA to manipulate those control parameters and search for an optimal set of values that minimize the discrepancy between simulation output and real field data.
This paper is devoted to the mathematical analysis and the numerical solution of data-driven optimization for an important class of fuzzy controllers, so-called Sugeno controllers. In contrast to other approaches whic...
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ISBN:
(纸本)0780383532
This paper is devoted to the mathematical analysis and the numerical solution of data-driven optimization for an important class of fuzzy controllers, so-called Sugeno controllers. In contrast to other approaches which optimize the underlying fuzzy sets, we will mainly focus on the linear approximation of the output variable according to the input data. While the first approach leads to nonlinear problems, the latter will result in a free, linear least squares system to be solved. Therefore this approach can be used for high dimensional problems as well, when due to the increasing complexity nonlinear systems are no longer applicable. By applying Tikhonov regularization we get stable and fast algorithms that create sufficiently optimized controllers;with saving their interpretability. Finally we will show, how variable selection can be used to increase interpretability and to reduce computation time.
An improved evolutionary algorithm is proposed to perform multiobjective dynamic optimization of a semi-batch styrene polymerization process. The target is to determine the optimal feeding trajectories and the reactor...
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An improved evolutionary algorithm is proposed to perform multiobjective dynamic optimization of a semi-batch styrene polymerization process. The target is to determine the optimal feeding trajectories and the reactor operating temperature, which maximize the monomer conversion rate and minimize the initiator residue concentration in the final product, The optimization problem has been formulated as a multi-objective mixed-integer nonlinear problem (MOMINLP). The proposed approach allows the effective computation of the optimal operating strategies for the production of polymers with the average molecular weight and the polydispersity index required.
This paper introduces a hybrid optimization algorithm, followed by a corresponding estimation technique, for the estimation of ARMAX models. The hybrid algorithm consists of a stochastic component and a deterministic ...
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ISBN:
(纸本)0791841731
This paper introduces a hybrid optimization algorithm, followed by a corresponding estimation technique, for the estimation of ARMAX models. The hybrid algorithm consists of a stochastic component and a deterministic counterpart and aims at combining high convergence rate together with reliability in the search for global optimum. The estimation procedure is slit in two phases, due to the mixed linear-nonlinear relationship between the residuals and the parameter vector, and results in stable and invertible models. The proposed methodology is implemented in the estimation of a half-car suspension model of a road vehicle, using noise-corrupted observations, and the results yield very stable performance of the hybrid algorithm, reduced computational cost, in comparison to conventional stochastic optimizationalgorithms, and ability to describe satisfactory system's dynamics.
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