ECE 571 – Advanced Microprocessor-Based Design Lecture 36
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High End Visualization with Scalable Display System
HIGH END VISUALIZATION WITH SCALABLE DISPLAY SYSTEM Dinesh M. Sarode*, Bose S.K.*, Dhekne P.S.*, Venkata P.P.K.*, Computer Division, Bhabha Atomic Research Centre, Mumbai, India Abstract display, then the large number of pixels shows the picture Today we can have huge datasets resulting from in greater details and interaction with it enables the computer simulations (CFD, physics, chemistry etc) and greater insight in understanding the data. However, the sensor measurements (medical, seismic and satellite). memory constraints, lack of the rendering power and the There is exponential growth in computational display resolution offered by even the most powerful requirements in scientific research. Modern parallel graphics workstation makes the visualization of this computers and Grid are providing the required magnitude difficult or impossible. computational power for the simulation runs. The rich While the cost-performance ratio for the component visualization is essential in interpreting the large, dynamic based on semiconductor technologies doubling in every data generated from these simulation runs. The 18 months or beyond that for graphics accelerator cards, visualization process maps these datasets onto graphical the display resolution is lagging far behind. The representations and then generates the pixel resolutions of the displays have been increasing at an representation. The large number of pixels shows the annual rate of 5% for the last two decades. The ability to picture in greater details and interaction with it enables scale the components: graphics accelerator and display by the greater insight on the part of user in understanding the combining them is the most cost-effective way to meet data more quickly, picking out small anomalies that could the ever-increasing demands for high resolution. -
Reverse Engineering Power Management on NVIDIA Gpus - Anatomy of an Autonomic-Ready System Martin Peres
Reverse Engineering Power Management on NVIDIA GPUs - Anatomy of an Autonomic-ready System Martin Peres To cite this version: Martin Peres. Reverse Engineering Power Management on NVIDIA GPUs - Anatomy of an Autonomic-ready System. ECRTS, Operating Systems Platforms for Embedded Real-Time appli- cations 2013, Jul 2013, Paris, France. hal-00853849 HAL Id: hal-00853849 https://hal.archives-ouvertes.fr/hal-00853849 Submitted on 23 Aug 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Reverse engineering power management on NVIDIA GPUs - Anatomy of an autonomic-ready system Martin Peres Ph.D. student at LaBRI University of Bordeaux Hobbyist Linux/Nouveau Developer Email: [email protected] Abstract—Research in power management is currently limited supported nor documented by NVIDIA. As GPUs are leading by the fact that companies do not release enough documentation the market in terms of performance-per-Watt [3], they are or interfaces to fully exploit the potential found in modern a good candidate for a reverse engineering effort of their processors. This problem is even more present in GPUs despite power management features. The choice of reverse engineering having the highest performance-per-Watt ratio found in today’s NVIDIA’s power management features makes sense as they processors. -
AMD Powerpoint- White Template
RDNA Architecture Forward-looking statement This presentation contains forward-looking statements concerning Advanced Micro Devices, Inc. (AMD) including, but not limited to, the features, functionality, performance, availability, timing, pricing, expectations and expected benefits of AMD’s current and future products, which are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are commonly identified by words such as "would," "may," "expects," "believes," "plans," "intends," "projects" and other terms with similar meaning. Investors are cautioned that the forward-looking statements in this presentation are based on current beliefs, assumptions and expectations, speak only as of the date of this presentation and involve risks and uncertainties that could cause actual results to differ materially from current expectations. Such statements are subject to certain known and unknown risks and uncertainties, many of which are difficult to predict and generally beyond AMD's control, that could cause actual results and other future events to differ materially from those expressed in, or implied or projected by, the forward-looking information and statements. Investors are urged to review in detail the risks and uncertainties in AMD's Securities and Exchange Commission filings, including but not limited to AMD's Quarterly Report on Form 10-Q for the quarter ended March 30, 2019 2 Highlights of the RDNA Workgroup Processor (WGP) ▪ Designed for lower latency and higher -
Report of Contributions
X.Org Developers Conference 2020 Report of Contributions https://xdc2020.x.org/e/XDC2020 X.Org Developer … / Report of Contributions State of text input on Wayland Contribution ID: 1 Type: not specified State of text input on Wayland Wednesday, 16 September 2020 20:15 (5 minutes) Between the last impromptu talk at GUADEC 2018, text input on Wayland has become more organized and more widely adopted. As before, the three-pronged approach of text_input, in- put_method, and virtual keyboard still causes confusion, but increased interest in implementing it helps find problems and come closer to something that really works for many usecases. The talk will mention how a broken assumption causes a broken protocol, and why we’re notdone with Wayland input methods yet. It’s recommended to people who want to know more about the current state of input methods on Wayland. Recommended background: aforementioned GUADEC talk, wayland-protocols reposi- tory, my blog: https://dcz_self.gitlab.io/ Code of Conduct Yes GSoC, EVoC or Outreachy No Primary author: DCZ, Dorota Session Classification: Demos / Lightning talks I Track Classification: Lightning Talk September 30, 2021 Page 1 X.Org Developer … / Report of Contributions IGT GPU Tools 2020 Update Contribution ID: 2 Type: not specified IGT GPU Tools 2020 Update Wednesday, 16 September 2020 20:00 (5 minutes) Short update on IGT - what has changed in the last year, where are we right now and what we have planned for the near future. IGT GPU Tools is a collection of tools and tests aiding development of DRM drivers. It’s widely used by Intel in its public CI system. -
SIMD Extensions
SIMD Extensions PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 12 May 2012 17:14:46 UTC Contents Articles SIMD 1 MMX (instruction set) 6 3DNow! 8 Streaming SIMD Extensions 12 SSE2 16 SSE3 18 SSSE3 20 SSE4 22 SSE5 26 Advanced Vector Extensions 28 CVT16 instruction set 31 XOP instruction set 31 References Article Sources and Contributors 33 Image Sources, Licenses and Contributors 34 Article Licenses License 35 SIMD 1 SIMD Single instruction Multiple instruction Single data SISD MISD Multiple data SIMD MIMD Single instruction, multiple data (SIMD), is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data simultaneously. Thus, such machines exploit data level parallelism. History The first use of SIMD instructions was in vector supercomputers of the early 1970s such as the CDC Star-100 and the Texas Instruments ASC, which could operate on a vector of data with a single instruction. Vector processing was especially popularized by Cray in the 1970s and 1980s. Vector-processing architectures are now considered separate from SIMD machines, based on the fact that vector machines processed the vectors one word at a time through pipelined processors (though still based on a single instruction), whereas modern SIMD machines process all elements of the vector simultaneously.[1] The first era of modern SIMD machines was characterized by massively parallel processing-style supercomputers such as the Thinking Machines CM-1 and CM-2. These machines had many limited-functionality processors that would work in parallel. -
AMD Firepro™Professional Graphics for CAD & Engineering and Media & Entertainment
AMD FirePro™Professional Graphics for CAD & Engineering and Media & Entertainment Performance at every price point. AMD FirePro professional graphics offer breakthrough capabilities that can help maximize productivity and help lower cost and complexity — giving you the edge you need in your business. Outstanding graphics performance, compute power and ultrahigh-resolution multidisplay capabilities allows broadcast, design and engineering professionals to work at a whole new level of detail, speed, responsiveness and creativity. AMD FireProTM W9100 AMD FireProTM W8100 With 16GB GDDR5 memory and the ability to support up to six 4K The new AMD FirePro W8100 workstation graphics card is based on displays via six Mini DisplayPort outputs,1 the AMD FirePro W9100 the AMD Graphics Core Next (GCN) GPU architecture and packs up graphics card is the ideal single-GPU solution for the next generation to 4.2 TFLOPS of compute power to accelerate your projects beyond of ultrahigh-resolution visualization environments. just graphics. AMD FireProTM W7100 AMD FireProTM W5100 The new AMD FirePro W7100 graphics card delivers 8GB The new AMD FirePro™ W5100 graphics card delivers optimized of memory, application performance and special features application and multidisplay performance for midrange users. that media and entertainment and design and engineering With 4GB of ultra-fast GDDR5 memory, users can tackle moderately professionals need to take their projects to the next level. complex models, assemblies, data sets or advanced visual effects with ease. AMD FireProTM W4100 AMD FireProTM W2100 In a class of its own, the AMD FirePro Professional graphics starts with AMD W4100 graphics card is the best choice FirePro W2100 graphics, delivering for entry-level users who need a boost in optimized and certified professional graphics performance to better address application performance that similarly- their evolving workflows. -
Order Independent Transparency in Opengl 4.X Christoph Kubisch – [email protected] TRANSPARENT EFFECTS
Order Independent Transparency In OpenGL 4.x Christoph Kubisch – [email protected] TRANSPARENT EFFECTS . Photorealism: – Glass, transmissive materials – Participating media (smoke...) – Simplification of hair rendering . Scientific Visualization – Reveal obscured objects – Show data in layers 2 THE CHALLENGE . Blending Operator is not commutative . Front to Back . Back to Front – Sorting objects not sufficient – Sorting triangles not sufficient . Very costly, also many state changes . Need to sort „fragments“ 3 RENDERING APPROACHES . OpenGL 4.x allows various one- or two-pass variants . Previous high quality approaches – Stochastic Transparency [Enderton et al.] – Depth Peeling [Everitt] 3 peel layers – Caveat: Multiple scene passes model courtesy of PTC required Peak ~84 layers 4 RECORD & SORT 4 2 3 1 . Render Opaque – Depth-buffer rejects occluded layout (early_fragment_tests) in; fragments 1 2 3 . Render Transparent 4 – Record color + depth uvec2(packUnorm4x8 (color), floatBitsToUint (gl_FragCoord.z) ); . Resolve Transparent 1 2 3 4 – Fullscreen sort & blend per pixel 4 2 3 1 5 RESOLVE . Fullscreen pass uvec2 fragments[K]; // encodes color and depth – Not efficient to globally sort all fragments per pixel n = load (fragments); sort (fragments,n); – Sort K nearest correctly via vec4 color = vec4(0); register array for (i < n) { blend (color, fragments[i]); – Blend fullscreen on top of } framebuffer gl_FragColor = color; 6 TAIL HANDLING . Tail Handling: – Discard Fragments > K – Blend below sorted and hope error is not obvious [Salvi et al.] . Many close low alpha values are problematic . May not be frame- coherent (flicker) if blend is not primitive- ordered K = 4 K = 4 K = 16 Tailblend 7 RECORD TECHNIQUES . Unbounded: – Record all fragments that fit in scratch buffer – Find & Sort K closest later + fast record - slow resolve - out of memory issues 8 HOW TO STORE . -
This Is Your Presentation Title
Introduction to GPU/Parallel Computing Ioannis E. Venetis University of Patras 1 Introduction to GPU/Parallel Computing www.prace-ri.eu Introduction to High Performance Systems 2 Introduction to GPU/Parallel Computing www.prace-ri.eu Wait, what? Aren’t we here to talk about GPUs? And how to program them with CUDA? Yes, but we need to understand their place and their purpose in modern High Performance Systems This will make it clear when it is beneficial to use them 3 Introduction to GPU/Parallel Computing www.prace-ri.eu Top 500 (June 2017) CPU Accel. Rmax Rpeak Power Rank Site System Cores Cores (TFlop/s) (TFlop/s) (kW) National Sunway TaihuLight - Sunway MPP, Supercomputing Center Sunway SW26010 260C 1.45GHz, 1 10.649.600 - 93.014,6 125.435,9 15.371 in Wuxi Sunway China NRCPC National Super Tianhe-2 (MilkyWay-2) - TH-IVB-FEP Computer Center in Cluster, Intel Xeon E5-2692 12C 2 Guangzhou 2.200GHz, TH Express-2, Intel Xeon 3.120.000 2.736.000 33.862,7 54.902,4 17.808 China Phi 31S1P NUDT Swiss National Piz Daint - Cray XC50, Xeon E5- Supercomputing Centre 2690v3 12C 2.6GHz, Aries interconnect 3 361.760 297.920 19.590,0 25.326,3 2.272 (CSCS) , NVIDIA Tesla P100 Cray Inc. DOE/SC/Oak Ridge Titan - Cray XK7 , Opteron 6274 16C National Laboratory 2.200GHz, Cray Gemini interconnect, 4 560.640 261.632 17.590,0 27.112,5 8.209 United States NVIDIA K20x Cray Inc. DOE/NNSA/LLNL Sequoia - BlueGene/Q, Power BQC 5 United States 16C 1.60 GHz, Custom 1.572.864 - 17.173,2 20.132,7 7.890 4 Introduction to GPU/ParallelIBM Computing www.prace-ri.eu How do -
GPU4S: Embedded Gpus in Space
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. “The final publication is available at: DOI: 10.1109/DSD.2019.00064 GPU4S: Embedded GPUs in Space Leonidas Kosmidis∗,Jer´ omeˆ Lachaizey, Jaume Abella∗ Olivier Notebaerty, Francisco J. Cazorla∗;z, David Steenarix ∗Barcelona Supercomputing Center (BSC), Spain yAirbus Defence and Space, France zSpanish National Research Council (IIIA-CSIC), Spain xEuropean Space Agency, The Netherlands Abstract—Following the same trend of automotive and avion- in space [1][2]. Those studies concluded that although their ics, the space domain is witnessing an increase in the on-board energy efficiency is high, their power consumption is an order computing performance demands. This raise in performance of magnitude higher than the limited power budget of a space needs comes from both control and payload parts of the space- craft and calls for advanced electronics able to provide high system, which is limited to a couple of Watts. computational power under the constraints of the harsh space Interestingly, GPUs entered in the embedded domain to environment. On the non-technical side, for strategic reasons it is satisfy the increasing demand for multimedia-based hand- mandatory to get European independence on the used computing held and consumer devices such as smartphones, in-vehicle technology. In this project, which is still in its early phases, we entertainment systems, televisions, set-top boxes etc. -
Best Practice for Mobile
If this doesn’t look familiar, you’re in the wrong conference 1 This section of the course is about the ways that mobile graphics hardware works, and how to work with this hardware to get the best performance. The focus here is on the Vulkan API because the explicit nature exposes the hardware, but the principles apply to other programming models. There are ways of getting better mobile efficiency by reducing what you’re drawing: running at lower frame rate or resolution, only redrawing what and when you need to, etc.; we’ve addressed those in previous years and you can find some content on the course web site, but this time the focus is on high-performance 3D rendering. Those other techniques still apply, but this talk is about how to keep the graphics moving and assuming you’re already doing what you can to reduce your workload. 2 Mobile graphics processing units are usually fundamentally different from classical desktop designs. * Mobile GPUs mostly (but not exclusively) use tiling, desktop GPUs tend to use immediate-mode renderers. * They use system RAM rather than dedicated local memory and a bus connection to the GPU. * They have multiple cores (but not so much hyperthreading), some of which may be optimised for low power rather than performance. * Vulkan and similar APIs make these features more visible to the developer. Here, we’re mostly using Vulkan for examples – but the architectural behaviour applies to other APIs. 3 Let’s start with the tiled renderer. There are many approaches to tiling, even within a company. -
Thermal Covert Channels Leveraging Package-On-Package DRAM
Thermal Covert Channels Leveraging Package-On-Package DRAM Shuai Chen∗, Wenjie Xiongy, Yehan Xu∗, Bing Li∗ and Jakub Szefery ∗Southeast University, Nanjing, China fchenshuai ic,220174472, bernie [email protected] yYale University, New Haven, CT, USA fwenjie.xiong, [email protected] Abstract—Package-on-Package (PoP) is an intergraded circuit observation of execution time [2], [3], or on observation packaging technique where multiple separate packages are of physical emanation, such as heat [4] or electromagnetic mounted vertically one on top of the other, allowing for more (EM) field [5], for example. This work focuses on thermal compact system design and reduction in the distance between channels, and shows how the heat, or temperature, can modules. However, this can introduce security vulnerabilities. be measured without special measurement equipment or In particular, this work shows that due to the close physical physical access in commodity SoC devices. Especially, this proximity of a System-on-a-Chip (SoC) package and a PoP work focuses on an SoC DRAM Package-on-Package (PoP) DRAM that is on top of it, a thermal covert channel exists configuration [6] where two chips (SoC and DRAM) are between the SoC and the PoP DRAM. The thermal covert stacked on top of each other. The heat transfers between the channel can allow multiple cores in the SoC to communicate two can be observed by measuring decay rate of DRAM by modulating the temperature of the PoP DRAM. Especially, cells, which is the basis for this work. it is possible for one core in the SoC to generate heat patterns, Previously, heat-based or thermal covert channels have which encode data that is to be transmitted, and another been demonstrated in data centers [7], or in multicore core to observe the transmitted pattern by measuring the processors [8], but these require dedicated thermal sensors decay rate of DRAM cells. -
PC-Grade Parallel Processing and Hardware Acceleration for Large-Scale Data Analysis
University of Huddersfield Repository Yang, Su PC-Grade Parallel Processing and Hardware Acceleration for Large-Scale Data Analysis Original Citation Yang, Su (2009) PC-Grade Parallel Processing and Hardware Acceleration for Large-Scale Data Analysis. Doctoral thesis, University of Huddersfield. This version is available at http://eprints.hud.ac.uk/id/eprint/8754/ The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or not-for-profit purposes without prior permission or charge, provided: • The authors, title and full bibliographic details is credited in any copy; • A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way. For more information, including our policy and submission procedure, please contact the Repository Team at: [email protected]. http://eprints.hud.ac.uk/ PC-Grade Parallel Processing and Hardware Acceleration for Large-Scale Data Analysis Yang Su A thesis submitted to the University of Huddersfield in partial fulfilment of the requirements for the degree of Doctor of Philosophy School of Computing and Engineering University of Huddersfield October 2009 Acknowledgments I would like to thank the School of Computing and Engineering at the University of Huddersfield for providing this great opportunity of study and facilitating me throughout this project.