Contributions of Hybrid Architectures to Depth Imaging: a CPU, APU and GPU Comparative Study
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Tesla K80 Gpu Accelerator
TESLA K80 GPU ACCELERATOR BD-07317-001_v05 | January 2015 Board Specification DOCUMENT CHANGE HISTORY BD-07317-001_v05 Version Date Authors Description of Change 01 June 23, 2014 GG, SM Preliminary Information (Information contained within this board specification is subject to change) 02 October 8, 2014 GG, SM • Updated product name • Minor change to Table 2 03 October 31, 2014 GG, SM • Added “8-Pin CPU Power Connector” section • Updated Figure 2 04 November 14, 2014 GG, SM • Removed preliminary and NDA • Updated boost clocks • Minor edits throughout document 05 January 30, 2015 GG, SM Updated Table 2 with MTBF data Tesla K80 GPU Accelerator BD-07317-001_v05 | ii TABLE OF CONTENTS Overview ............................................................................................. 1 Key Features ...................................................................................... 2 NVIDIA GPU Boost on Tesla K80 ................................................................ 3 Environmental Conditions ....................................................................... 4 Configuration ..................................................................................... 5 Mechanical Specifications ........................................................................ 6 PCI Express System ............................................................................... 6 Tesla K80 Bracket ................................................................................ 7 8-Pin CPU Power Connector ................................................................... -
Small Form Factor 3D Graphics for Your Pc
VisionTek Part# 900701 PRODUCTIVITY SERIES: SMALL FORM FACTOR 3D GRAPHICS FOR YOUR PC The VisionTek Radeon R7 240SFF graphics card offers a perfect balance of performance, features, and affordability for the gamer seeking a complete solution. It offers support for the DIRECTX® 11.2 graphics standard and 4K Ultra HD for stunning 3D visual effects, realistic lighting, and lifelike imagery. Its Short Form Factor design enables it to fit into the latest Low Profile desktops and workstations, yet the R7 240SFF can be converted to a standard ATX design with the included tall bracket. With 2GB of DDR3 memory and award-winning Graphics Core Next (GCN) architecture, and DVI-D/HDMI outputs, the VisionTek Radeon R7 240SFF is big on features and light on your wallet. RADEON R7 240 SPECS • Graphics Engine: RADEON R7 240 • Video Memory: 2GB DDR3 • Memory Interface: 128bit • DirectX® Support: 11.2 • Bus Standard: PCI Express 3.0 • Core Speed: 780MHz • Memory Speed: 800MHz x2 • VGA Output: VGA* • DVI Output: SL DVI-D • HDMI Output: HDMI (Video/Audio) • UEFI Ready: Support SYSTEM REQUIREMENTS • PCI Express® based PC is required with one X16 lane graphics slot available on the motherboard. • 400W (or greater) power supply GCN Architecture: A new design for AMD’s unified graphics processing and compute cores that allows recommended. 500 Watt for AMD them to achieve higher utilization for improved performance and efficiency. CrossFire™ technology in dual mode. • Minimum 1GB of system memory. 4K Ultra HD Support: Experience what you’ve been missing even at 1080P! With support for 3840 x • Installation software requires CD-ROM 2160 output via the HDMI port, textures and other detail normally compressed for lower resolutions drive. -
AMD Accelerated Parallel Processing Opencl Programming Guide
AMD Accelerated Parallel Processing OpenCL Programming Guide November 2013 rev2.7 © 2013 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, AMD Accelerated Parallel Processing, the AMD Accelerated Parallel Processing logo, ATI, the ATI logo, Radeon, FireStream, FirePro, Catalyst, and combinations thereof are trade- marks of Advanced Micro Devices, Inc. Microsoft, Visual Studio, Windows, and Windows Vista are registered trademarks of Microsoft Corporation in the U.S. and/or other jurisdic- tions. Other names are for informational purposes only and may be trademarks of their respective owners. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos. The contents of this document are provided in connection with Advanced Micro Devices, Inc. (“AMD”) products. AMD makes no representations or warranties with respect to the accuracy or completeness of the contents of this publication and reserves the right to make changes to specifications and product descriptions at any time without notice. The information contained herein may be of a preliminary or advance nature and is subject to change without notice. No license, whether express, implied, arising by estoppel or other- wise, to any intellectual property rights is granted by this publication. Except as set forth in AMD’s Standard Terms and Conditions of Sale, AMD assumes no liability whatsoever, and disclaims any express or implied warranty, relating to its products including, but not limited to, the implied warranty of merchantability, fitness for a particular purpose, or infringement of any intellectual property right. AMD’s products are not designed, intended, authorized or warranted for use as compo- nents in systems intended for surgical implant into the body, or in other applications intended to support or sustain life, or in any other application in which the failure of AMD’s product could create a situation where personal injury, death, or severe property or envi- ronmental damage may occur. -
AMD Radeon E8860
Components for AMD’s Embedded Radeon™ E8860 GPU INTRODUCTION The E8860 Embedded Radeon GPU available from CoreAVI is comprised of temperature screened GPUs, safety certi- fiable OpenGL®-based drivers, and safety certifiable GPU tools which have been pre-integrated and validated together to significantly de-risk the challenges typically faced when integrating hardware and software components. The plat- form is an off-the-shelf foundation upon which safety certifiable applications can be built with confidence. Figure 1: CoreAVI Support for E8860 GPU EXTENDED TEMPERATURE RANGE CoreAVI provides extended temperature versions of the E8860 GPU to facilitate its use in rugged embedded applications. CoreAVI functionally tests the E8860 over -40C Tj to +105 Tj, increasing the manufacturing yield for hardware suppliers while reducing supply delays to end customers. coreavi.com [email protected] Revision - 13Nov2020 1 E8860 GPU LONG TERM SUPPLY AND SUPPORT CoreAVI has provided consistent and dedicated support for the supply and use of the AMD embedded GPUs within the rugged Mil/Aero/Avionics market segment for over a decade. With the E8860, CoreAVI will continue that focused support to ensure that the software, hardware and long-life support are provided to meet the needs of customers’ system life cy- cles. CoreAVI has extensive environmentally controlled storage facilities which are used to store the GPUs supplied to the Mil/ Aero/Avionics marketplace, ensuring that a ready supply is available for the duration of any program. CoreAVI also provides the post Last Time Buy storage of GPUs and is often able to provide additional quantities of com- ponents when COTS hardware partners receive increased volume for existing products / systems requiring additional inventory. -
A Review of Gpuopen Effects
A REVIEW OF GPUOPEN EFFECTS TAKAHIRO HARADA & JASON LACROIX • An initiative designed to help developers make better content by “opening up” the GPU • Contains a variety of software modules across various GPU needs: • Effects and render features • Tools, SDKs, and libraries • Patches and drivers • Software hosted on GitHub with no “black box” implementations or licensing fees • Website provides: • The latest news and information on all GPUOpen software • Tutorials and samples to help you optimise your game • A central location for up-to-date GPU and CPU documentation • Information about upcoming events and previous presentations AMD Public | Let’s build… 2020 | A Review of GPUOpen Effects | May 15, 2020 | 2 LET’S BUILD A NEW GPUOPEN… • Brand new, modern, dynamic website • Easy to find the information you need quickly • Read the latest news and see what’s popular • Learn new tips and techniques from our engineers • Looks good on mobile platforms too! • New social media presence • @GPUOpen AMD Public | Let’s build… 2020 | A Review of GPUOpen Effects | May 15, 2020 | 3 EFFECTS A look at recently released samples AMD Public | Let’s build… 2020 | A Review of GPUOpen Effects | May 15, 2020 | 4 TRESSFX 4.1 • Self-contained solution for hair simulation • Implementation into Radeon® Cauldron framework • DirectX® 12 and Vulkan® with full source • Optimized physics simulation • Faster velocity shock propagation • Simplified local shape constraints • Reorganization of dispatches • StrandUV support • New LOD system • New and improved Autodesk® Maya® -
HP Grafične Postaje: HP Z1, HP Z2, HP Z4, ……
ARES RAČUNALNIŠTVO d.o.o. HP Z1 Tržaška cesta 330 HP Z2 1000 Ljubljana, Slovenia, EU HP Z4 tel: 01-256 21 50 Grafične kartice GSM: 041-662 508 e-mail: [email protected] www.ares-rac.si 29.09.2021 DDV = 22% 1,22 Grafične postaje HP Grafične postaje: HP Z1, HP Z2, HP Z4, …….. ZALOGA Nudimo vam tudi druge modele, ki še niso v ceniku preveri zalogo Na koncu cenika so tudi opcije: Grafične kartice. Ostale opcije po ponudbi. Objavljamo neto ceno in ceno z DDV (PPC in akcijsko). Dokončna cena in dobavni rok - po konkretni ponudbi Objavljamo neto ceno in ceno z DDV (PPC in akcijsko). Dokončna cena po ponudbi. Koda HP Z1 TWR Grafična postaja /Delovna postaja Zaloga Neto cena Cena z DDV (EUR) (EUR) HP Z1 G8 TWR Zaloga Neto cena Cena z DDV 29Y17AV HP Z1 G8 TWR, Core i5-11500, 16GB, SSD 512GB, nVidia GeForce RTX 3070 8GB, USB-C, Win10Pro #71595950 Koda: 29Y17AV#71595950 HP delovna postaja, HP grafična postaja, Procesor Intel Core i5 -11500 (2,7 - 4,6 GHz) 12MB 6 jedr/12 niti Nabor vezja Intel Q570 Pomnilnik 16 GB DDR4 3200 MHz (1x16GB), 3x prosta reža, do 128 GB 1.735,18 2.116,92 SSD pogon 512 GB M.2 2280 PCIe NVMe TLC Optična enota: brez, HDD pogon : brez Razširitvena mesta 2x 3,5'', 1x 2,5'', RAID podpira RAID AKCIJA 1.657,00 2.021,54 Grafična kartica: nVidia GeForce RTX 3070 8GB GDDR6, 256bit, 5888 Cuda jeder CENA Žične povezave: Intel I219-LM Gigabit Network Brezžične povezave: brez Razširitve: 1x M.2 2230; 1x PCIe 3.0 x16; 2x PCIe 3,0 x16 (ožičena kot x4); 2x M.2 2230/2280; 2x PCIe 3.0 x1 Čitalec kartic: brez Priključki spredaj: 1x USB-C, 2x -
Workstation Fisse E Mobili Monitor Hub Usb-C Docking
OCCUPATEVI DEL VOSTRO BUSINESS, NOI CI PRENDEREMO CURA DEI VOSTRI COMPUTERS E PROGETTI INFORMATICI 16-09-21 16 WORKSTATION FISSE E MOBILI MONITOR HUB USB-C DOCKING STATION Noleggiare computer, server, dispositivi informatici e di rete, Cremonaufficio è un’azienda informatica presente nel terri- può essere una buona alternativa all’acquisto, molti infatti torio cremonese dal 1986. sono i vantaggi derivanti da questa pratica e il funziona- Sin dai primi anni si è distinta per professionalità e capacità, mento è molto semplice. Vediamo insieme alcuni di questi registrando di anno in anno un trend costante di crescita. vantaggi: Oltre venticinque anni di attività di vendita ed assistenza di Vantaggi Fiscali. Grazie al noleggio è possibile ottenere di- prodotti informatici, fotocopiatrici digitali, impianti telefonici, versi benefici in termini di fiscalità, elemento sempre molto reti dati fonia, videosorveglianza, hanno consolidato ed affer- interessante per le aziende. Noleggiare computer, server e mato l’azienda Cremonaufficio come fornitrice di prodotti e dispositivi di rete permette di ottenere una riduzione sulla servizi ad alto profilo qualitativo. tassazione annuale e il costo del noleggio è interamente Il servizio di assistenza tecnica viene svolto da tecnici interni, deducibile. specializzati nei vari settori, automuniti. Locazione operativa, non locazione finanziaria. A differen- Cremonaufficio è un operatore MultiBrand specializzato in za del leasing, il noleggio non prevede l’iscrizione ad una Information Technology ed Office Automation e, come tale, si centrale rischi, di conseguenza migliora il rating creditizio, avvale di un sistema di gestione operativa certificato ISO 9001. facilita il rapporto con le banche e l’accesso ai finanzia- menti. -
Monte Carlo Evaluation of Financial Options Using a GPU a Thesis
Monte Carlo Evaluation of Financial Options using a GPU Claus Jespersen 20093084 A thesis presented for the degree of Master of Science Computer Science Department Aarhus University Denmark 02-02-2015 Supervisor: Gerth Brodal Abstract The financial sector has in the last decades introduced several new fi- nancial instruments. Among these instruments, are the financial options, which for some cases can be difficult if not impossible to evaluate analyti- cally. In those cases the Monte Carlo method can be used for pricing these instruments. The Monte Carlo method is a computationally expensive al- gorithm for pricing options, but is at the same time an embarrassingly parallel algorithm. Modern Graphical Processing Units (GPU) can be used for general purpose parallel-computing, and the Monte Carlo method is an ideal candidate for GPU acceleration. In this thesis, we will evaluate the classical vanilla European option, an arithmetic Asian option, and an Up-and-out barrier option using the Monte Carlo method accelerated on a GPU. We consider two scenarios; a single option evaluation, and a se- quence of a varying amount of option evaluations. We report performance speedups of up to 290x versus a single threaded CPU implementation and up to 53x versus a multi threaded CPU implementation. 1 Contents I Theoretical aspects of Computational Finance 5 1 Computational Finance 5 1.1 Options . .7 1.1.1 Types of options . .7 1.1.2 Exotic options . .9 1.2 Pricing of options . 11 1.2.1 The Black-Scholes Partial Differential Equation . 11 1.2.2 Solving the PDE and pricing vanilla European options . -
Mairjason2015phd.Pdf (1.650Mb)
Power Modelling in Multicore Computing Jason Mair a thesis submitted for the degree of Doctor of Philosophy at the University of Otago, Dunedin, New Zealand. 2015 Abstract Power consumption has long been a concern for portable consumer electron- ics, but has recently become an increasing concern for larger, power-hungry systems such as servers and clusters. This concern has arisen from the asso- ciated financial cost and environmental impact, where the cost of powering and cooling a large-scale system deployment can be on the order of mil- lions of dollars a year. Such a substantial power consumption additionally contributes significant levels of greenhouse gas emissions. Therefore, software-based power management policies have been used to more effectively manage a system’s power consumption. However, man- aging power consumption requires fine-grained power values for evaluating the run-time tradeoff between power and performance. Despite hardware power meters providing a convenient and accurate source of power val- ues, they are incapable of providing the fine-grained, per-application power measurements required in power management. To meet this challenge, this thesis proposes a novel power modelling method called W-Classifier. In this method, a parameterised micro-benchmark is designed to reproduce a selection of representative, synthetic workloads for quantifying the relationship between key performance events and the cor- responding power values. Using the micro-benchmark enables W-Classifier to be application independent, which is a novel feature of the method. To improve accuracy, W-Classifier uses run-time workload classification and derives a collection of workload-specific linear functions for power estima- tion, which is another novel feature for power modelling. -
A Statistical Performance Model of the Opteron Processor
A Statistical Performance Model of the Opteron Processor Jeanine Cook Jonathan Cook Waleed Alkohlani New Mexico State University New Mexico State University New Mexico State University Klipsch School of Electrical Department of Computer Klipsch School of Electrical and Computer Engineering Science and Computer Engineering [email protected] [email protected] [email protected] ABSTRACT m5 [11], Simics/GEMS [25], and more recently MARSSx86 [5]{ Cycle-accurate simulation is the dominant methodology for the only one supporting the popular x86 architecture. Some processor design space analysis and performance prediction. of these have not been validated against real hardware in However, with the prevalence of multi-core, multi-threaded the multi-core mode (SESC and M5), and the others that architectures, this method has become highly impractical as are validated show large errors of up to 50% [5]. All cycle- the sole means for design due to its extreme slowdowns. We accurate multi-core simulators are very slow; a few hundred have developed a statistical technique for modeling multi- thousand simulated instructions per second is the most op- core processors that is based on Monte Carlo methods. Us- timistic speed with the help of native mode execution. Fur- ing this method, processor models of contemporary archi- ther, the architectures supported by many of these simula- tectures can be developed and applied to performance pre- tors are now obsolete. diction, bottleneck detection, and limited design space anal- ysis. To date, we have accurately modeled the IBM Cell, the To address these issues and to satisfy our own desire for Intel Itanium, and the Sun Niagara 1 and Niagara 2 proces- performance models of contemporary processors, we devel- sors [34, 33, 10]. -
Power Modeling
CHAPTER 5 POWER MODELING Jason Mair1, Zhiyi Huang1, David Eyers1, Leandro Cupertino2, Georges Da Costa2, Jean-Marc Pierson2 and Helmut Hlavacs3 1Department of Computer Science, University of Otago, New Zealand 2Institute for Research in Informatics of Toulouse (IRIT), University of Toulouse III, France 3Faculty of Computer Science, University of Vienna, Austria 5.1 Introduction Power consumption has long been a concern for portable consumer electronics, with many manufacturers explicitly seeking to maximize battery life in order to improve the usability of devices such as laptops and smart phones. However, it has recently become a concern in the domain of much larger, more power hungry systems such as servers, clusters and data centers. This new drive to improve energy efficiency is in part due to the increasing deployment of large-scale systems in more businesses and industries, which have two pri- mary motives for saving energy. Firstly, there is the traditional economic incentive for a business to reduce their operating costs, where the cost of powering and cooling a large data center can be on the order of millions of dollars [18]. Reducing the total cost of own- ership for servers could help to stimulate further deployments. As servers become more affordable, deployments will increase in businesses where concerns over lifetime costs pre- viously prevented adoption. The second motivating factor is the increasing awareness of the environmental impact—e.g. greenhouse gas emissions—caused by power production. Reducing energy consumption can help a business indirectly reduce their environmental impact, making them more clean and green. The most commonly adopted solution for reducing power consumption is a hardware- based approach, where old, inefficient hardware is replaced with newer, more energy effi- COST Action 0804, edition. -
Deep Dive: Asynchronous Compute
Deep Dive: Asynchronous Compute Stephan Hodes Developer Technology Engineer, AMD Alex Dunn Developer Technology Engineer, NVIDIA Joint Session AMD NVIDIA ● Graphics Core Next (GCN) ● Maxwell, Pascal ● Compute Unit (CU) ● Streaming Multiprocessor (SM) ● Wavefronts ● Warps 2 Terminology Asynchronous: Not independent, async work shares HW Work Pairing: Items of GPU work that execute simultaneously Async. Tax: Overhead cost associated with asynchronous compute 3 Async Compute More Performance 4 Queue Fundamentals 3 Queue Types: 3D ● Copy/DMA Queue ● Compute Queue COMPUTE ● Graphics Queue COPY All run asynchronously! 5 General Advice ● Always profile! 3D ● Can make or break perf ● Maintain non-async paths COMPUTE ● Profile async on/off ● Some HW won’t support async ● ‘Member hyper-threading? COPY ● Similar rules apply ● Avoid throttling shared HW resources 6 Regime Pairing Good Pairing Poor Pairing Graphics Compute Graphics Compute Shadow Render Light culling G-Buffer SSAO (Geometry (ALU heavy) (Bandwidth (Bandwidth limited) limited) limited) (Technique pairing doesn’t have to be 1-to-1) 7 - Red Flags Problem/Solution Format Topics: ● Resource Contention - ● Descriptor heaps - ● Synchronization models ● Avoiding “async-compute tax” 8 Hardware Details - ● 4 SIMD per CU ● Up to 10 Wavefronts scheduled per SIMD ● Accomplish latency hiding ● Graphics and Compute can execute simultanesouly on same CU ● Graphics workloads usually have priority over Compute 9 Resource Contention – Problem: Per SIMD resources are shared between Wavefronts SIMD executes