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（总结篇）使用MATLABGPU加速计算MATLAB并行计算与分布式服务器MATLAB技术论坛本帖最后由蓝云风翼于 2013-12-18 17:28 编辑注：利用gpu加速有一下工具1.JACKET 可从帖子中寻找2.MATLAB a.并行计算工具箱 gpuArray，查看支持gpuArray的函数methods('gpuArray')b.已经支持GPU的一些工具箱c.使用mex方式 /thread-33511-1-1.htmld.使用产生ptx方法编写cuda kernel这些都可以通过help gpuArray查看，建议使用最新版本2013a 查看GPU是否支持gpuDevice命令3.GPUMAT帖子中找4. nvmex方式即cudawhitepaper可从帖子中直接下载/thread-20597-1-1.html/thread-25951-1-1.htmlSIMULINK :/forum.php?mod=viewthread&tid=27230目前，GPU在通用数值计算领域的应用已经越来越广泛，MATLAB通过以下几种方式支持GPU。
一、MATLAB内嵌GPU函数fft, filter,及linear algebra operations等。
二、内嵌工具箱支持GPU的函数: Communications System Toolbox, Neural Network Toolbox, Phased Array Systems Toolbox, and Signal Processing Toolbox (GPU support for signal processing algorithms)三、在MATLAB中接入CUDA kernel,通过PTX方式或者MEX方式。
Multiple GPUs在单机和计算集群上的使用通过MATLAB 的并行计算工具箱（PCT）及MATLAB分布式计算工具箱（MDCS）（matlab worker）一、PCT GPUArrayParallel Computing Toolbox 提供 GPUArray，这是一个具有多个关联函数的特殊数组类型，可让您直接从MATLAB 在启用CUDA 的 NVIDIA GPU 上执行计算。
代号分类号学号密级10701TP37公开1102121253题(中、英文)目基于GPU/多核CPU平台下并行计算的实时超分辨和立体视图生成Real-time Super-resolution and Stereoscopic View Genera-tion with GPU/Multicore CPU Based Parallel Computing 作者姓名孙增增指导教师姓名、职务郑喆坤教授学科门类工学提交论文日期二〇一四年三月学科、专业模式识别与智能系统西安电子科技大学学位论文独创性（或创新性）声明秉承学校严谨的学风和优良的科学道德，本人声明所呈交的论文是我个人在导师指导下进行的研究工作及取得的研究成果。
尽我所知，除了文中特别加以标注和致谢中所罗列的内容以外，论文中不包含其他人已经发表或撰写过的研究成果；也不包含为获得西安电子科技大学或其它教育机构的学位或证书而使用过的材料。
与我一同工作的同志对本研究所做的任何贡献均已在论文中做了明确的说明并表示了谢意。
申请学位论文与资料若有不实之处，本人承担一切的法律责任。
本人签名：日期：西安电子科技大学关于论文使用授权的说明本人完全了解西安电子科技大学有关保留和使用学位论文的规定，即：研究生在校攻读学位期间论文工作的知识产权单位属西安电子科技大学。
学校有权保留送交论文的复印件，允许查阅和借阅论文；学校可以公布论文的全部或部分内容，可以允许采用影印、缩印或其它复制手段保存论文。
同时本人保证，毕业后结合学位论文研究课题再撰写的文章一律署名单位为西安电子科技大学。
（保密的论文在解密后遵守此规定）本人授权西安电子科技大学图书馆保存学位论文，本学位论文属于(保密级别)，在年解密后适用本授权书，并同意将论文在互联网上发布。
本人签名：日期：导师签名：日期：摘要近些年来，许多因素导致了计算产业转向了并行化发展的方向。
在这过程中，受市场对实时、高清晰3维图形绘制的需求驱使，可编程的图形处理单元（GPU）逐渐发展进化成为了具有强大计算能力、非常高内存带宽的高度并行、多线程的众核处理器。
rtx2009RTX 2009: A Revolutionary Leap Forward in Graphics Processing PowerIntroductionThe release of the RTX 2009 graphics card marked a significant milestone in the field of graphics processing power. Developed by Nvidia, this graphics card introduced groundbreaking features and capabilities that pushed the boundaries of gaming and visualization. In this document, we will explore the key features, advancements, and benefits of the RTX 2009 graphics card, demonstrating how it revolutionized the industry.1. Increased Performance and EfficiencyOne of the most remarkable aspects of the RTX 2009 graphics card is its substantial increase in performance and efficiency compared to its predecessors. Powered by the latest Turing architecture, the RTX 2009 delivers remarkable improvements in graphics processing unit (GPU) performance,allowing users to experience immersive gaming environments and realistic visual effects with ease.The introduction of real-time ray tracing technology was a game-changer in the RTX 2009. This technology enables the card to simulate the behavior of light in real-time, resulting in unparalleled visual realism. With ray tracing, every reflection, refraction, and shadow is accurately rendered, enhancing the overall quality of graphics in games and simulations.2. AI AccelerationThe RTX 2009 graphics card also features next-generation tensor cores, which enable AI acceleration. These tensor cores provide immense computational power for deep learning and neural network applications. With this capability, users can experience AI-powered features such as AI denoising, AI-enhanced upscaling, and AI-augmented game physics.This integration of AI acceleration technology opens up new possibilities for game developers, content creators, and researchers to explore innovative techniques and deliver cutting-edge experiences. It significantly reduces the time required for complex computations and enhances the overall efficiency of AI-based applications.3. Enhanced Gaming ExperienceThe RTX 2009 graphics card brings a host of features that enhance the gaming experience to new heights. With its advanced capabilities, gamers can now enjoy high frame rates, sharp image quality, and smooth gameplay, even in the most demanding titles.The inclusion of DLSS (Deep Learning Super Sampling) in the graphics card further enhances the visual quality and performance. DLSS leverages AI to upscale lower-resolution images in real-time, resulting in crisp and detailed visuals without compromising performance. This technology allows gamers to play games at higher resolutions and settings, providing a more immersive and lifelike experience.4. Virtual Reality (VR) SupportThe RTX 2009 graphics card has been optimized for virtual reality applications, opening the doors to a new level of immersion and realism. With its powerful GPU and dedicated VR support, the RTX 2009 delivers smooth and responsive performance in VR environments.The card's ability to render high-resolution imagery at high refresh rates ensures a seamless VR experience, reducing the risk of motion sickness and enhancing overall comfort. Gamers and content creators can now explore virtual worlds, interact with virtual objects, and experience gaming like never before.5. Future-Proofed TechnologyThe RTX 2009 graphics card embraces forward-looking technology, making it future-proofed and ready for upcoming advancements. Its support for hardware-accelerated ray tracing and AI processing ensures compatibility with future applications and games that leverage these technologies.Additionally, the card's powerful architecture and generous memory capacity provide the necessary resources for handling increasingly complex graphics and workloads. This future-proofing enables users to enjoy the latest advancements in gaming and visualization without the need for immediate hardware upgrades.ConclusionThe RTX 2009 graphics card by Nvidia offers a groundbreaking leap forward in graphics processing power. With its increased performance, real-time ray tracing, AI acceleration, enhanced gaming experience, VR support, and future-proofed technology, the RTX 2009 revolutionizes the way we experience graphics in gaming, simulation, and virtual reality. This graphics card sets a new standard for graphics processing power and paves the way for exciting advancements in the field. Whether you are a gamer, content creator, or researcher, the RTX 2009 is a game-changer that delivers unrivaled performance and realism.。
收稿日期：2008-09-25基金项目：国家自然科学基金资助项目（60673028）；国家自然科学基金国际合作交流项目（60573174）；合肥市科技局资助项目（合科合同（））作者简介：刘晓平（6），男，山东济南人，教授，主要研究方向为建模、仿真与协同计算。
自然现象的模拟一直是计算机图形学研究的热点，其中水面的模拟对自然场景的意义尤为重要。
由于水面的物理原型十分复杂，实时地对其进行精确描述相当困难，因此研究者们一直在寻求真实感和复杂度之间的平衡点。
文献[1]中Nick Foster 等人建立了基于Navier-Stokes 方程的水面模型，模型精确但求解复杂，不适合实时计算。
文献[2]例举了线性生成海面网格的方法，采用逆FFT 计算，求得一定数量的线性函数叠加描述波浪，该类方法追求统计学上的模拟，未考虑2010年工程图学学报2010第1期J OURNAL OF ENG INEERING GRAPHICSNo.1实时水面模拟方法研究刘晓平，谢文军（合肥工业大学计算机与信息学院VCC 研究室，安徽合肥230009）摘要：提出了一种基于GPU 的水面实时模拟方法。
该方法不依赖于噪声图，而实现了实时的水波生成、折射和反射效果的菲涅耳合成以及水面光照模型的计算。
利用GPU 在片段处理前的光栅化处理，该方法渲染负荷不会因水面大小和精度而增大。
且依赖GPU 的高速计算能力，方法可以达到实时。
关键词：计算机应用；实时水面模拟；光照模型；投影纹理中图分类号：TP 391文献标识码：A文章编号：1003-0158(2010)01-0079-05Research on the Method of Simulation of Real-Time WaterSurface Based on GPULIU Xiao-ping,XIE Wen-jun(VCC Division,School of Computer and Information,Hefei University of Technology,Hefei Anhui 230009,China )Abstr act:A method of real-time water surface simulation based on GPU is ing this method real-time water wave can be constructed without displacement texture,and then be rendered with the Fresnel effect and the illumination model.As the rasterization is done before the fragment processing of GPU,the burden of rendering will not grow with the size and precision of water area.According to the GPU ’s high capability of calculating,this method can achieve the real-time rendering.K ey words:computer application;real-time water surface simulation;illumination model;projective texture mapping20071004194-物理精确性，有待于与流体动力学理论的结合；而在游戏等需要快速生成水体的场合，往往采用perlin噪声[3]等噪声源的预计算生成水面，该方法可以生成视觉效果良好的水面，但无法结合物理计算。
TB-09377-001-v01 | January 2019 Technical Brief09377-001-v01TABLE OF CONTENTSPowering Any Virtual Workload (1)High-Performance Quadro Virtual Workstations (3)Deep Learning Inferencing (5)Virtual Desktops for Knowledge Workers (7)Summary (8)The NVIDIA® T4 graphics processing unit (GPU), based on the latest NVIDIA Turing™architecture, is now supported for virtualized workloads with NVIDIA virtual GPU(vGPU) software. Using the same NVIDIA graphics drivers that are deployed on non-virtualized systems, NVIDIA vGPU software provides Virtual Machines (VMs) with thesame breakthrough performance and versatility that the T4 offers to a physicalenvironment.NVIDIA initially launched T4 at GTC Japan in the Fall of 2018 as an AI inferencingplatform for bare metal servers. When T4 was initially released, it was specificallydesigned to meet the needs of public and private cloud environments as their scalability requirements continue to grow. Since then there has been rapid adoption and it was recently released on the Google Cloud Platform. The T4 is the most universal graphics processing unit (GPU) to date -- capable of running any workload to drive greater data center efficiency. In a bare metal environment, T4 accelerates diverse workloads including deep learning training and inferencing. Adding support for virtual desktops with NVIDIA GRID® Virtual PC (GRID vPC) and NVIDIA Quadro® Virtual Data Center Workstation (Quadro vDWS) software is the next level of workflow acceleration.The T4 has a low-profile, single slot form factor, roughly the size of a cell phone, anddraws a maximum of 70 W power, so it requires no supplemental power connector. This highly efficient design allows NVIDIA vGPU customers to reduce their operating costs considerably and offers the flexibility to scale their vGPU deployment by installing additional GPUs in a server, because two T4 GPUs can fit into the same space as a single NVIDIA® Tesla® M10 or Tesla M60 GPU, which could consume more than 3X the power.Powering Any Virtual WorkloadFigure 1. NVIDIA Tesla GPUs for Virtualization WorkloadsThe NVIDIA T4 leverages the NVIDIA Turing™ architecture – the biggest architectural leap forward in over a decade – enabling major advances in efficiency and performance. Some of the key features provided by the NVIDIA Turing architecture include Tensor Cores for accelerating deep learning inference workflows as well as NVIDIA® CUDA®cores, Tensor Cores, and RT Cores for real-time ray tracing acceleration and batch rendering. It’s also the first GPU architecture to support GDDR6 memory, which provides improved performance and power efficiency versus the previous generation GDDR5.The T4 is an NVIDIA RTX™-capable GPU, benefiting from all of the enhancements of the NVIDIA RTX platform, including:④Real-time ray tracing④Accelerated batch rendering④AI-enhanced denoising④Photorealistic design with accurate shadows, reflections, and refractionsThe T4 is well suited for a wide range of data center workloads including:④Virtual Desktops for knowledge workers using modern productivity applications④Virtual Workstations for scientists, engineers, and creative professionals④Deep Learning Inferencing and trainingThe graphics performance of the NVIDIA T4 directly benefits virtual workstations implemented with NVIDIA Quadro vDWS software to run rendering and simulation workloads. Users of high-end applications, such as CATIA, SOLIDWORKS, and ArcGIS Pro, are typically segmented as light, medium or heavy based on the type of workflow they’re running and the size of the model/data they are working with. The T4 is a low-profile, single slot card for light and medium users working with mid-to-large sized models. T4 offers double the amount of framebuffer (16 GB) versus the previous generation Tesla P4 (8 GB) card, therefore users can work with bigger models within their virtual workstations. Benchmark results show that T4 with Quadro vDWS delivers 25% faster performance than Tesla P4 and offers almost twice the professional graphics performance of the NVIDIA Tesla M60.High-Performance Quadro Virtual WorkstationsFigure 2. T4 Performance Comparison with Tesla M60 and Tesla P4 Based on SPECviewperf13The NVIDIA Turing architecture of the T4 fuses real-time ray tracing, AI, simulation, and rasterization to fundamentally change computer graphics. Dedicated ray-tracing processors called RT Cores accelerate the computation of how light travels in 3D environments. NVIDIA Turing accelerates real-time ray tracing over the previous-generation NVIDIA® Pascal™ architecture and can render final frames for film effects faster than CPUs. The new Tensor Cores, processors that accelerate deep learning training and inference, accelerate AI-enhanced graphics features—such as denoising, resolution scaling, and video re-timing—creating applications with powerful new capabilities.Figure 3. Benefits of Real-Time Rendering with NVIDIA RTX TechnologyThe T4 with the NVIDIA Turing architecture sets a new bar for power efficiency and performance for deep learning and AI. Its multi-precision tensor cores combined with accelerated containerized software stacks from NVIDIA GPU Cloud (NGC) delivers revolutionary performance.As we are racing towards a future where every customer inquiry, every product and service will be touched and improved by AI, NVIDIA vGPU is bringing Deep Learning inferencing and training workflows to virtual machines. Quadro vDWS users can now execute inferencing workloads within their VDI sessions by accessing NGC containers. NGC integrates GPU-optimized deep learning frameworks, runtimes, libraries and even the OS into a ready-to-run container, available at no charge. NGC simplifies and standardizes deployment, making it easier and quicker for data scientists to build, train and deploy AI models. Accessing NGC containers within a VM offers even more portability and security to virtual users for classroom environments and virtual labs. Test results show that Quadro vDWS users leveraging T4 can run deep learning inferencing workloads 25X faster than with CPU-only VMs.Deep Learning InferencingFigure 4. Run Video Inferencing Workloads up to 25X Faster with T4 and Quadro vDWS vs. a CPU-only VMBenchmark test results show that the T4 is a universal GPU which can run a variety of workloads, including virtual desktops for knowledge workers accessing modern productivity applications. Modern productivity applications, high resolution and multiple monitors, and Windows 10 continue to require more graphics and with NVIDIA GRID vPC software, combined with NVIDIA Tesla GPUs, users can achieve a native-PC experience in a virtualized environment. While the Tesla M10 GPU, combined with NVIDIA GRID software, remains the ideal solution to provide optimal user density, TCO and performance for knowledge workers in a VDI environment, the versatility of the T4 makes it an attractive solution as well.The Tesla M10 was announced in Spring of 2016 and offers the best user density and performance option for NVIDIA GRID vPC customers. The Tesla M10 is a 32 GB dual-slot card which draws up to 225 W of power, therefore requires a supplemental power connector. The T4 is a low profile, 16 GB single-slot card, which draws 70 W maximum and does not require a supplemental power connector.Two NVIDIA T4 GPUs provide 32 GB of framebuffer and support the same user density as a single Tesla M10 with 32 GB of framebuffer, but with lower power consumption. While the Tesla M10 provides the best value for knowledge worker deployments, selecting the T4 for this use case brings the unique benefits of the NVIDIA Turing architecture. This enables IT to maximize data center resources by running virtual desktops in addition to virtual workstations, deep learning inferencing, rendering, and other graphics and compute intensive workloads -- all leveraging the same data center infrastructure. This ability to run mixed workloads can increase user productivity, maximize utilization, and reduce costs in the data center. Additional T4 technology enhancements include support for VP9 decode, which is often used for video playback, and H.265 (HEVC) 4:4:4 encode/decode.The flexible design of the T4 makes it well suited for any data center workload -enabling IT to leverage it for multiple use cases and maximize efficiency and utilization.It is perfectly aligned for vGPU implementations - delivering a native-PC experience for virtualized productivity applications, untethering architects, engineers and designersfrom their desks, and enabling deep learning inferencing workloads from anywhere, onany device. This universal GPU can be deployed on industry-standard servers to provide graphics and compute acceleration across any workload and future-proof the data center. Its dense, low power form factor can improve data center operating expenses while improving performance and efficiency and scales easily as compute and graphics needs grow.NoticeThe information provided in this specification is believed to be accurate and reliable as of the date provided. However, NVIDIA Corporation (“NVIDIA”) does not give any representations or warranties, expressed or implied, as to the accuracy or completeness of such information. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. 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计算机科学与技术学院粒子系统综述报告姓名：学号：指导教师：年月日摘要如何逼真地模拟自然景物一直是图形学中的一个热门研究课题和难点问题。
火焰、云烟、滴、雪花等动态自然景物的模拟，在航空航天、影视广告、虚拟场景中有着广泛的应用。
然而多数景物的外形是随机变化的，很难用常规的建模方法及模拟技术来描述。
因此自然景物的模拟一直以来都是虚拟现实领域研究的热点和重点。
随着近年来研究的不断深入，各种自然景物模拟算法不断涌现，模拟结果也越来越具有真实感。
其中，粒子系统方法是迄今为止被认为模拟不规则模糊自然景物最为成功的一种生成算法。
关键字：图形学；粒子系统；虚拟现实；真实感引言虚拟现实(简称VR),又称灵境技术,是以沉浸感、交互性和构想为基本特征的计算机高级人机界面。
现今,从军用到民用,从工业到商业,从自然景观虚拟到人文景观虚拟,虚拟现实技术的应用越来越广[1]。
随着应用的不断扩展,在虚拟现实系统的设计与实现中,有一些景观很难用简单的几何图元来表示,这类景观主要是一些离散的或者动态的自然景观和人文景观,例如烟、星星、喷泉和烟花等等[2]。
1983年由W. T. Reeves等首次系统地提出了粒子系统方法[3]。
此方法被认为是迄今为止模拟不规则模糊物体，最为成功的一种图形生成算法。
在计算机虚拟仿真领域，应用粒子系统模拟不规则模糊物体的方法，已经得到了广泛应用。
本文通过对粒子系统的阐述、研究现状、建模及仿真以及对模型的优化，有了一个详细地描述，从而使大家对粒子系统的研究现状，有了更为直接的了解，最后通过分析现有粒子系统研究现状的不足，对于粒子系统的进一步研究，提出了自己的看法。
1 粒子系统的概念及其研究现状1.1 什么是粒子系统粒子系统是一种典型的物理建模系统，它是用简单的体素完成复杂运动的建模[4]。
粒子系统由大量称为粒子的简单体素构成，每个粒子具有位置、速度、颜色和生命期等属性，这些属性可根据动力学计算和随机过程得到。
英伟达工程模拟解决方案IntroductionNVIDIA is a leading technology company that is revolutionizing the way we interact with computers, from data centers to personal devices. One of NVIDIA’s core focuses is on engineering simulation, which is the process of using computer software to model and test the behavior of complex systems and processes. This helps engineers and scientists to better understand and optimize their designs before they are built, ultimately saving time and money. In this paper, we will explore t he various aspects of NVIDIA’s engineering simulation solution, including its benefits, applications, and case studies.Benefits of NVIDIA’s Engineering Simulation SolutionNVIDIA’s engineering simulation solution offers a number of benefits to engineers a nd scientists, including:1. Faster simulations: NVIDIA’s powerful hardware, such as the NVIDIA Quadro RTX graphics cards, provides the necessary computational power to run complex simulations more quickly than ever before. This allows engineers to iterate on their designs more rapidly, ultimately leading to better end products.2. High-fidelity results: NVIDIA’s hardware and software offer the ability to produce highly accurate and detailed simulations, providing engineers with a deeper understanding of their designs and how they will perform in the real world.3. Scalability: NVIDIA’s engineering simulation solution is designed to scale seamlessly from small laptop-sized systems to large multi-GPU workstations and data center servers. This allows engineers to perform simulations at the scale that best fits their needs, without being limited by hardware constraints.4. Integration with popular simulation software: NVIDIA’s hardware and software solutions are designed to work seamlessly with popular engineering simulation software packages, such as ANSYS, COMSOL, and Abaqus. This ensures that engineers can make the most of their existing simulation workflows while taking advantage of NVIDIA’s hardware acceleration.5. Support for real-time simulation: NVIDIA’s engineering simulation solution is capable of running simulations in real-time, providing engineers with immediate feedback on their designs and allowing them to interact with the simulation as it runs.Applications of NVIDIA’s Engineering Simulation Solu tionNVIDIA’s engineering simulation solution has a wide range of applications across various industries and domains, including:1. Aerospace and defense: Engineers in the aerospace and defense industries use engineering simulation to model the performance of aircraft, spacecraft, and weapons systems. This allows them to optimize their designs for maximum efficiency and safety.2. Automotive: Automotive engineers use engineering simulation to model the performance of vehicles, including crash simulations, aerodynamics, and thermal management. This allows them to design safer, more fuel-efficient, and better-performing vehicles.3. Energy: Engineers in the energy industry use engineering simulation to model the behavior of power plants, renewable energy systems, and energy storage devices. This allows them to optimize the performance and reliability of these systems.4. Electronics: Engineers in the electronics industry use engineering simulation to model the behavior of electronic components and systems, including circuit boards, semiconductors, and electromagnetic fields. This allows them to design more efficient and reliable electronics.5. Biomedical: Biomedical engineers use engineering simulation to model the behavior of biological systems, medical devices, and drug delivery systems. This allows them to optimize the performance and safety of these systems.Case StudiesTo further illustrate the benefits and applications of NVIDIA’s engineering simulation solution, let’s take a look at a few case studies:1. Aerospace and defense: A leading aerospace and defense company used NVIDIA’s engineering simulation solution to model the performance of a new aircraft design. By using NVIDIA’s hardware acceleration, they were able to run simulations more quickly and at a higher level of detail than ever before. This allowed them to identify potential design flaws early in the process, ultimately saving time and money during the development phase.2. Automotive: A major automotive manufacturer used NVIDIA’s engineering s imulation solution to model the crash performance of a new vehicle design. By leveraging NVIDIA’s real-time simulation capabilities, they were able to make rapid design iterations and instantly see the impact on the vehicle’s safety performance. This allow ed them to develop a safer vehicle in a shorter amount of time.3. Energy: An energy company used NVIDIA’s engineering simulation solution to model the performance of a new solar power plant. By using NVIDIA’s hardware acceleration, they were able to run simulations at a larger scale and with more complexConclusionNVIDIA’s engineering simulation solution offers a powerful set of tools that enable engineers and scientists to model and optimize their designs more effectively than ever before. With its fast simulations, high-fidelity results, scalability, integration with popularsimulation software, and support for real-time simulation, NVIDIA’s engineering simulation solution is well-suited for a wide range of applications across industries. The case studies presented in this paper demonstrate how NVIDIA’s engineering simulation solution has been successfully used in aerospace and defense, automotive, and energy applications, ultimately leading to more efficient and safer designs. As engineering simulations continue to play a crucial role in modern product development, NVIDIA’s engineering simulation solution is poised to remain at the forefront of this industry.。
