ATI Radeon™ HD 4870 Computation Highlights
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Lecture 7 CUDA
Lecture 7 CUDA Dr. Wilson Rivera ICOM 6025: High Performance Computing Electrical and Computer Engineering Department University of Puerto Rico Outline • GPU vs CPU • CUDA execution Model • CUDA Types • CUDA programming • CUDA Timer ICOM 6025: High Performance Computing 2 CUDA • Compute Unified Device Architecture – Designed and developed by NVIDIA – Data parallel programming interface to GPUs • Requires an NVIDIA GPU (GeForce, Tesla, Quadro) ICOM 4036: Programming Languages 3 CUDA SDK GPU and CPU: The Differences ALU ALU Control ALU ALU Cache DRAM DRAM CPU GPU • GPU – More transistors devoted to computation, instead of caching or flow control – Threads are extremely lightweight • Very little creation overhead – Suitable for data-intensive computation • High arithmetic/memory operation ratio Grids and Blocks Host • Kernel executed as a grid of thread Device blocks Grid 1 – All threads share data memory Kernel Block Block Block space 1 (0, 0) (1, 0) (2, 0) • Thread block is a batch of threads, Block Block Block can cooperate with each other by: (0, 1) (1, 1) (2, 1) – Synchronizing their execution: For hazard-free shared Grid 2 memory accesses Kernel 2 – Efficiently sharing data through a low latency shared memory Block (1, 1) • Two threads from two different blocks cannot cooperate Thread Thread Thread Thread Thread (0, 0) (1, 0) (2, 0) (3, 0) (4, 0) – (Unless thru slow global Thread Thread Thread Thread Thread memory) (0, 1) (1, 1) (2, 1) (3, 1) (4, 1) • Threads and blocks have IDs Thread Thread Thread Thread Thread (0, 2) (1, 2) (2, -
AMD Firestream™ 9350 GPU Compute Accelerator
AMD FireStream™ 9350 GPU Compute Accelerator High Density Server GPU Acceleration The AMD FireStream™ 9350 GPU compute accelerator card offers industry- leading performance-per-watt in a highly dense, single-slot form factor. This AMD FireStream 9350 low-profile GPU card is designed for scalable high performance computing > Heterogeneous computing that (HPC) systems that require density and power efficiency. The AMD FireStream leverages AMD GPUs and x86 CPUs 9350 is ideal for a wide range of HPC applications across several industries > High performance per watt at 2.9 including Finance, Energy (Oil and Gas), Geosciences, Life Sciences, GFLOPS / Watt Manufacturing (CAE, CFD, etc.), Defense, and more. > Industry’s most dense server GPU card 1 Utilizing the multi-billion-transistor ASICs developed for AMD Radeon™ graphics > Low profile and passively cooled cards, AMD’s FireStream 9350 cards provide maximum performance-per-slot > Massively parallel, programmable GPU and are designed to meet demanding performance and reliability requirements architecture of HPC systems that can scale to thousands of nodes. AMD FireStream 9350 > Open standard OpenCL™ and cards include a single DisplayPort output. DirectCompute2 AMD FireStream 9350 cards can be used in scalable servers, blades, PCIe® > 2.0 TFLOPS, Single-Precision Peak chassis, and are available from many leading server OEMs and HPC solution > 400 GFLOPS, Double-Precision Peak providers. > 2GB GDDR5 memory Priced competitively, AMD FireStream GPUs offer unparalleled performance > Industry’s only Single-Slot PCIe® Accelerator and value for high performance computing. > PCI Express® 2.1 Compliant Note: For workstation and server > AMD Accelerated Parallel Processing installations that require active (APP) Technology SDK with OpenCL3 cooling, AMD FirePro™ Professional > 3-year planned availability; 3-year Graphics boards are software- limited warranty compatible and offer comparable features. -
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 Opencl User Guide.)
AMD Accelerated Parallel Processing OpenCLUser Guide December 2014 rev1.0 © 2014 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. -
Novel Methodologies for Predictable CPU-To-GPU Command Offloading
Novel Methodologies for Predictable CPU-To-GPU Command Offloading Roberto Cavicchioli Università di Modena e Reggio Emilia, Italy [email protected] Nicola Capodieci Università di Modena e Reggio Emilia, Italy [email protected] Marco Solieri Università di Modena e Reggio Emilia, Italy [email protected] Marko Bertogna Università di Modena e Reggio Emilia, Italy [email protected] Abstract There is an increasing industrial and academic interest towards a more predictable characterization of real-time tasks on high-performance heterogeneous embedded platforms, where a host system offloads parallel workloads to an integrated accelerator, such as General Purpose-Graphic Processing Units (GP-GPUs). In this paper, we analyze an important aspect that has not yet been considered in the real-time literature, and that may significantly affect real-time performance if not properly treated, i.e., the time spent by the CPU for submitting GP-GPU operations. We will show that the impact of CPU-to-GPU kernel submissions may be indeed relevant for typical real-time workloads, and that it should be properly factored in when deriving an integrated schedulability analysis for the considered platforms. This is the case when an application is composed of many small and consecutive GPU com- pute/copy operations. While existing techniques mitigate this issue by batching kernel calls into a reduced number of persistent kernel invocations, in this work we present and evaluate three other approaches that are made possible by recently released versions of the NVIDIA CUDA GP-GPU API, and by Vulkan, a novel open standard GPU API that allows an improved control of GPU com- mand submissions. -
Download Radeon Grpadhics Catalylist and Driver AMD Catalyst Graphics Driver 13.4 X64
download radeon grpadhics catalylist and driver AMD Catalyst Graphics Driver 13.4 x64. Fixes : - With the Radeon HD7790 graphics card, corruption may be seen in the game Tomb Raider with TressFX enabled - Texture flickering may be observed in DirectX 9.0c enabled applications - Corruption observed in Tomb Raider with TressFX enabled for AMD Crossfire and single GPU system configurations - Corruption observed on objects and textures in the game, Call of Duty – Black Ops 2 - Corruption observed in the game, Alan Wake (DirectX 9 Mode) when attempting to change in-game settings with Stereoscopic 3D using Tridef - Corruption observed on textures in the game, Battle Field: Bad Company 2 with forced anti-aliasing enabled - Adobe Photoshop CS6 experiences screen flicker under Windows 8 based system - Horizontal line corruption may be observed with forced anti-aliasing enabled in the game, Dishonored - System hang may occur when scrolling through the game selection menu in the game, DiRT 3 - F1 2012 may crash to desktop on systems supporting AMD PowerXpress (Enduro) Technology - Support added for AMD Radeon HD7790 and AMD Radeon HD 7990 - Catalyst Driver optimizations to improve performance in Far Cry 3, Crysis 3, and 3DMark - Significantly improves latency performance in Skyrim, Boderlands 2, Guild Wars 2, Tomb Raider and Hitman Absolution. Performance gains seen with the entire AMD Radeon HD 7000 Series for the following: - Batman Arkham City (1920x1200): Performance improvements up to 5% - Borderlands 2 (2560x1600): Performance improvements -
An Evolution of Mobile Graphics
AN EVOLUTION OF MOBILE GRAPHICS Michael C. Shebanow Vice President, Advanced Processor Lab Samsung Electronics July 20, 20131 DISCLAIMER • The views herein are my own • They do not represent Samsung’s vision nor product plans 2 • The Mobile Market • Review of GPU Tech • GPU Efficiency • User Experience • Tech Challenges • Summary 3 The Rise of the Mobile GPU & Connectivity A NEW WORLD COMING? 4 DISCRETE GPU MARKET Flattening 5 MOBILE GPU MARKET Smart • In 2012, an estimated 800+ Phones million mobile GPUs shipped “Phablets” • ~123M tablets • ~712M smart phones Tablets • Will easily exceed 1B in the coming years • Trend: • Discrete GPU relatively flat • Mobile is growing rapidly 6 WW INTERNET TRAFFIC • Source: Cisco VNI Mobile INET IP Traffic growth Traffic • Internet traffic growth Year (TB/sec) rate (TB/sec) rate is staggering 2005 0.9 0.00 2006 1.5 65% 0.00 • 2012 total traffic is 2007 2.5 61% 0.01 13.7 GB per person 2008 3.8 54% 0.01 per month 2009 5.6 45% 0.04 2010 7.8 40% 0.10 • 2012 smart phone 2011 10.6 36% 0.23 traffic at 2012 12.4 17% 0.34 0.342 GB per person per month • 2017 smart phone traffic expected at 2.7 GB per person per month 7 WHERE ARE WE HEADED?… • Enormous quantity of GPUs • Large amount of interconnectivity • Better I/O 8 GPU Pipelines A BRIEF REVIEW OF GPU TECH 9 MOBILE GPU PIPELINE ARCHITECTURES Tile-based immediate mode rendering IA VS CCV RS PS ROP (TBIMR) Tile-based deferred IA VS CCV scene rendering (TBDR) RS PS ROP IA = input assembler VS = vertex shader CCV = cull, clip, viewport transform RS = rasterization, -
NVIDIA Quadro P4000
NVIDIA Quadro P4000 GP104 1792 112 64 8192 MB GDDR5 256 bit GRAPHICS PROCESSOR CORES TMUS ROPS MEMORY SIZE MEMORY TYPE BUS WIDTH The Quadro P4000 is a professional graphics card by NVIDIA, launched in February 2017. Built on the 16 nm process, and based on the GP104 graphics processor, the card supports DirectX 12.0. The GP104 graphics processor is a large chip with a die area of 314 mm² and 7,200 million transistors. Unlike the fully unlocked GeForce GTX 1080, which uses the same GPU but has all 2560 shaders enabled, NVIDIA has disabled some shading units on the Quadro P4000 to reach the product's target shader count. It features 1792 shading units, 112 texture mapping units and 64 ROPs. NVIDIA has placed 8,192 MB GDDR5 memory on the card, which are connected using a 256‐bit memory interface. The GPU is operating at a frequency of 1227 MHz, which can be boosted up to 1480 MHz, memory is running at 1502 MHz. We recommend the NVIDIA Quadro P4000 for gaming with highest details at resolutions up to, and including, 5760x1080. Being a single‐slot card, the NVIDIA Quadro P4000 draws power from 1x 6‐pin power connectors, with power draw rated at 105 W maximum. Display outputs include: 4x DisplayPort. Quadro P4000 is connected to the rest of the system using a PCIe 3.0 x16 interface. The card measures 241 mm in length, and features a single‐slot cooling solution. Graphics Processor Graphics Card GPU Name: GP104 Released: Feb 6th, 2017 Architecture: Pascal Production Active Status: Process Size: 16 nm Bus Interface: PCIe 3.0 x16 Transistors: 7,200 -
Graphics Card Support List
Graphics card support list Device Name Chipset ASUS GTXTITAN-6GD5 NVIDIA GeForce GTX TITAN ZOTAC GTX980 NVIDIA GeForce GTX980 ASUS GTX980-4GD5 NVIDIA GeForce GTX980 MSI GTX980-4GD5 NVIDIA GeForce GTX980 Gigabyte GV-N980D5-4GD-B NVIDIA GeForce GTX980 MSI GTX970 GAMING 4G GOLDEN EDITION NVIDIA GeForce GTX970 Gigabyte GV-N970IXOC-4GD NVIDIA GeForce GTX970 ASUS GTX780TI-3GD5 NVIDIA GeForce GTX780Ti ASUS GTX770-DC2OC-2GD5 NVIDIA GeForce GTX770 ASUS GTX760-DC2OC-2GD5 NVIDIA GeForce GTX760 ASUS GTX750TI-OC-2GD5 NVIDIA GeForce GTX750Ti ASUS ENGTX560-Ti-DCII/2D1-1GD5/1G NVIDIA GeForce GTX560Ti Gigabyte GV-NTITAN-6GD-B NVIDIA GeForce GTX TITAN Gigabyte GV-N78TWF3-3GD NVIDIA GeForce GTX780Ti Gigabyte GV-N780WF3-3GD NVIDIA GeForce GTX780 Gigabyte GV-N760OC-4GD NVIDIA GeForce GTX760 Gigabyte GV-N75TOC-2GI NVIDIA GeForce GTX750Ti MSI NTITAN-6GD5 NVIDIA GeForce GTX TITAN MSI GTX 780Ti 3GD5 NVIDIA GeForce GTX780Ti MSI N780-3GD5 NVIDIA GeForce GTX780 MSI N770-2GD5/OC NVIDIA GeForce GTX770 MSI N760-2GD5 NVIDIA GeForce GTX760 MSI N750 TF 1GD5/OC NVIDIA GeForce GTX750 MSI GTX680-2GB/DDR5 NVIDIA GeForce GTX680 MSI N660Ti-PE-2GD5-OC/2G-DDR5 NVIDIA GeForce GTX660Ti MSI N680GTX Twin Frozr 2GD5/OC NVIDIA GeForce GTX680 GIGABYTE GV-N670OC-2GD NVIDIA GeForce GTX670 GIGABYTE GV-N650OC-1GI/1G-DDR5 NVIDIA GeForce GTX650 GIGABYTE GV-N590D5-3GD-B NVIDIA GeForce GTX590 MSI N580GTX-M2D15D5/1.5G NVIDIA GeForce GTX580 MSI N465GTX-M2D1G-B NVIDIA GeForce GTX465 LEADTEK GTX275/896M-DDR3 NVIDIA GeForce GTX275 LEADTEK PX8800 GTX TDH NVIDIA GeForce 8800GTX GIGABYTE GV-N26-896H-B -
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. -
Graphics: Mesa, AMDVLK, Adreno and Protected Xe Path
Published on Tux Machines (http://www.tuxmachines.org) Home > content > Graphics: Mesa, AMDVLK, Adreno and Protected Xe Path Graphics: Mesa, AMDVLK, Adreno and Protected Xe Path By Roy Schestowitz Created 08/02/2021 - 11:48pm Submitted by Roy Schestowitz on Monday 8th of February 2021 11:48:11 PM Filed under Graphics/Benchmarks [1] Panfrost Gallium3D Lands Its New Bifrost Scheduler In Mesa 21.1 - Phoronix[2] Hitting Mesa 21.1 this morning is a scheduler implementation for Panfrost Gallium3D, the open-source Arm Mali graphics driver. Lead Panfrost developer Alyssa Rosenzweig has been working to implement a scheduler in panfrost for the Arm Bifrost graphics code path. The scheduler has been in the works for a number of months and is passing the relevant conformance tests and has now been merged. AMDVLK 2021.Q1.3 Brings Performance Tuning For War Thunder - Phoronix[3] AMDVLK 2021.Q1.3 is out this morning as the latest snapshot of the official open-source AMD Radeon Vulkan driver for Linux systems that is derived from their shared platform driver sources. AMDVLK 2021.Q1.3 is on the lighter side with AMDVLK 2021.Q1.2 having arrived just over one week ago. Of the two listed driver changes, AMDVLK 2021.Q1.3 is rebuilt against the Vulkan API 1.2.168 headers. Freedreno's MSM DRM Driver Adds More Adreno Support, Speedbin Capability For Linux 5.12 - Phoronix[4] The MSM Direct Rendering Manager driver originally developed as part of the Freedreno effort for open-source Qualcomm Adreno graphics on Linux while now supported by the likes of Google and Qualcomm's Code Aurora engineers has some notable changes in store for the next Linux kernel cycle. -
Graphics Processing Units
Graphics Processing Units Graphics Processing Units (GPUs) are coprocessors that traditionally perform the rendering of 2-dimensional and 3-dimensional graphics information for display on a screen. In particular computer games request more and more realistic real-time rendering of graphics data and so GPUs became more and more powerful highly parallel specialist computing units. It did not take long until programmers realized that this computational power can also be used for tasks other than computer graphics. For example already in 1990 Lengyel, Re- ichert, Donald, and Greenberg used GPUs for real-time robot motion planning [43]. In 2003 Harris introduced the term general-purpose computations on GPUs (GPGPU) [28] for such non-graphics applications running on GPUs. At that time programming general-purpose computations on GPUs meant expressing all algorithms in terms of operations on graphics data, pixels and vectors. This was feasible for speed-critical small programs and for algorithms that operate on vectors of floating-point values in a similar way as graphics data is typically processed in the rendering pipeline. The programming paradigm shifted when the two main GPU manufacturers, NVIDIA and AMD, changed the hardware architecture from a dedicated graphics-rendering pipeline to a multi-core computing platform, implemented shader algorithms of the rendering pipeline in software running on these cores, and explic- itly supported general-purpose computations on GPUs by offering programming languages and software- development toolchains. This chapter first gives an introduction to the architectures of these modern GPUs and the tools and languages to program them. Then it highlights several applications of GPUs related to information security with a focus on applications in cryptography and cryptanalysis.