Intel Processor Graphics Gen11 Architecture
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Release Notes for X11R6.8.2 the X.Orgfoundation the Xfree86 Project, Inc
Release Notes for X11R6.8.2 The X.OrgFoundation The XFree86 Project, Inc. 9February 2005 Abstract These release notes contains information about features and their status in the X.Org Foundation X11R6.8.2 release. It is based on the XFree86 4.4RC2 RELNOTES docu- ment published by The XFree86™ Project, Inc. Thereare significant updates and dif- ferences in the X.Orgrelease as noted below. 1. Introduction to the X11R6.8.2 Release The release numbering is based on the original MIT X numbering system. X11refers to the ver- sion of the network protocol that the X Window system is based on: Version 11was first released in 1988 and has been stable for 15 years, with only upwardcompatible additions to the coreX protocol, a recordofstability envied in computing. Formal releases of X started with X version 9 from MIT;the first commercial X products werebased on X version 10. The MIT X Consortium and its successors, the X Consortium, the Open Group X Project Team, and the X.OrgGroup released versions X11R3 through X11R6.6, beforethe founding of the X.OrgFoundation. Therewill be futuremaintenance releases in the X11R6.8.x series. However,efforts arewell underway to split the X distribution into its modular components to allow for easier maintenance and independent updates. We expect a transitional period while both X11R6.8 releases arebeing fielded and the modular release completed and deployed while both will be available as different consumers of X technology have different constraints on deployment. Wehave not yet decided how the modular X releases will be numbered. We encourage you to submit bug fixes and enhancements to bugzilla.freedesktop.orgusing the xorgproduct, and discussions on this server take place on <[email protected]>. -
Mac Labs. More Creativity
Mac labs. More creativity. More engagement. More learning. Every Mac comes ready for 21st-century learning. Mac OS X Server creates the ultimate lab environment. Creativity and innovation are critical skills in the modern Mac OS X Server includes simplified tools for creating wikis and workplace, which makes them critical skills to teach today’s blogs, so students and staff can set up and manage them with students. Apple offers comprehensive lab solutions for your little or no help from IT. The Spotlight Server feature makes it school or university that will creatively engage students and easy for students collaborating on projects to find and view motivate them to take an interest in their own learning. The content stored anywhere on the network. Of course, access iLife ’09 suite of applications comes standard on every Mac, controls are built in, so they only get the search results they’re so students can immediately begin creating podcasts, editing authorised to see. Mac OS X Server also allows for real-time videos, composing music, producing photo essays and more. streaming of audio and video content, which lets everyone get right to work instead of waiting for downloads – saving major Mac or PC? Have both. storage across your network. Now one lab really can solve all your computing needs. Every Mac is powered by an Intel processor and features Launch careers from your Mac lab. Mac OS X – the world’s most advanced operating system. As an Apple Authorised Training Centre for Education, your Mac OS X comes with an amazing dual-boot feature called school can build a bridge between students and real-world Boot Camp that lets students run Windows XP or Vista natively careers. -
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, -
Opengl Distilled / Paul Martz
Page left blank intently OpenGL® Distilled By Paul Martz ............................................... Publisher: Addison Wesley Professional Pub Date: February 27, 2006 Print ISBN-10: 0-321-33679-8 Print ISBN-13: 978-0-321-33679-8 Pages: 304 Table of Contents | Inde OpenGL opens the door to the world of high-quality, high-performance 3D computer graphics. The preferred application programming interface for developing 3D applications, OpenGL is widely used in video game development, visuali,ation and simulation, CAD, virtual reality, modeling, and computer-generated animation. OpenGL® Distilled provides the fundamental information you need to start programming 3D graphics, from setting up an OpenGL development environment to creating realistic te tures and shadows. .ritten in an engaging, easy-to-follow style, this boo/ ma/es it easy to find the information you0re loo/ing for. 1ou0ll quic/ly learn the essential and most-often-used features of OpenGL 2.0, along with the best coding practices and troubleshooting tips. Topics include Drawing and rendering geometric data such as points, lines, and polygons Controlling color and lighting to create elegant graphics Creating and orienting views Increasing image realism with te ture mapping and shadows Improving rendering performance Preserving graphics integrity across platforms A companion .eb site includes complete source code e amples, color versions of special effects described in the boo/, and additional resources. Page left blank intently Table of contents: Chapter 6. Texture Mapping Copyright ............................................................... 4 Section 6.1. Using Texture Maps ........................... 138 Foreword ............................................................... 6 Section 6.2. Lighting and Shadows with Texture .. 155 Preface ................................................................... 7 Section 6.3. Debugging .......................................... 169 About the Book .................................................... -
ATI Radeon™ HD 4870 Computation Highlights
AMD Entering the Golden Age of Heterogeneous Computing Michael Mantor Senior GPU Compute Architect / Fellow AMD Graphics Product Group [email protected] 1 The 4 Pillars of massively parallel compute offload •Performance M’Moore’s Law Î 2x < 18 Month s Frequency\Power\Complexity Wall •Power Parallel Î Opportunity for growth •Price • Programming Models GPU is the first successful massively parallel COMMODITY architecture with a programming model that managgped to tame 1000’s of parallel threads in hardware to perform useful work efficiently 2 Quick recap of where we are – Perf, Power, Price ATI Radeon™ HD 4850 4x Performance/w and Performance/mm² in a year ATI Radeon™ X1800 XT ATI Radeon™ HD 3850 ATI Radeon™ HD 2900 XT ATI Radeon™ X1900 XTX ATI Radeon™ X1950 PRO 3 Source of GigaFLOPS per watt: maximum theoretical performance divided by maximum board power. Source of GigaFLOPS per $: maximum theoretical performance divided by price as reported on www.buy.com as of 9/24/08 ATI Radeon™HD 4850 Designed to Perform in Single Slot SP Compute Power 1.0 T-FLOPS DP Compute Power 200 G-FLOPS Core Clock Speed 625 Mhz Stream Processors 800 Memory Type GDDR3 Memory Capacity 512 MB Max Board Power 110W Memory Bandwidth 64 GB/Sec 4 ATI Radeon™HD 4870 First Graphics with GDDR5 SP Compute Power 1.2 T-FLOPS DP Compute Power 240 G-FLOPS Core Clock Speed 750 Mhz Stream Processors 800 Memory Type GDDR5 3.6Gbps Memory Capacity 512 MB Max Board Power 160 W Memory Bandwidth 115.2 GB/Sec 5 ATI Radeon™HD 4870 X2 Incredible Balance of Performance,,, Power, Price -
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. -
Integrated Step-Down Converter with SVID Interface for Intel® CPU
Product Order Technical Tools & Support & Folder Now Documents Software Community TPS53820 SLUSE33 –FEBRUARY 2020 Integrated Step-Down Converter with SVID Interface for Intel® CPU Power Supply 1 Features 3 Description The TPS53820 device is D-CAP+ mode integrated 1• Single chip to support Intel VR13.HC SVID POL applications step-down converter for low current SVID rails of Intel CPU power supply. It provides up to two outputs to • Two outputs to support VCCANA (5.5 A) and power the low current SVID rails such as VCCANA P1V8 (4 A) (5.5 A) and P1V8 (4 A). The device employs D-CAP+ • D-CAP+™ Control for Fast Transient Response mode control to provide fast load transient • Wide Input Voltage (4.5 V to 15 V) performance. Internal compensation allows ease of use and reduces external components. • Differential Remote Sense • Programmable Internal Loop Compensation The device also provides telemetry, including input voltage, output voltage, output current and • Per-Phase Cycle-by-Cycle Current Limit temperature reporting. Over voltage, over current and • Programmable Frequency from 800 kHz to 2 MHz over temperature protections are provided as well. 2 • I C System Interface for Telemetry of Voltage, The TPS53820 device is packaged in a thermally Current, Output Power, Temperature, and Fault enhanced 35-pin QFN and operates between –40°C Conditions and 125°C. • Over-Current, Over-Voltage, Over-Temperature (1) protections Table 1. Device Information • Low Quiescent Current PART NUMBER PACKAGE BODY SIZE (NOM) • 5 mm × 5 mm, 35-Pin QFN, PowerPAD Package TPS53820 RWZ (35) 5 mm × 5 mm (1) For all available packages, see the orderable addendum at 2 Applications the end of the data sheet. -
Introduction to Intel® FPGA IP Cores
Introduction to Intel® FPGA IP Cores Updated for Intel® Quartus® Prime Design Suite: 20.3 Subscribe UG-01056 | 2020.11.09 Send Feedback Latest document on the web: PDF | HTML Contents Contents 1. Introduction to Intel® FPGA IP Cores..............................................................................3 1.1. IP Catalog and Parameter Editor.............................................................................. 4 1.1.1. The Parameter Editor................................................................................. 5 1.2. Installing and Licensing Intel FPGA IP Cores.............................................................. 5 1.2.1. Intel FPGA IP Evaluation Mode.....................................................................6 1.2.2. Checking the IP License Status.................................................................... 8 1.2.3. Intel FPGA IP Versioning............................................................................. 9 1.2.4. Adding IP to IP Catalog...............................................................................9 1.3. Best Practices for Intel FPGA IP..............................................................................10 1.4. IP General Settings.............................................................................................. 11 1.5. Generating IP Cores (Intel Quartus Prime Pro Edition)...............................................12 1.5.1. IP Core Generation Output (Intel Quartus Prime Pro Edition)..........................13 1.5.2. Scripting IP Core Generation.................................................................... -
Introducing the New 11Th Gen Intel® Core™ Desktop Processors
Product Brief 11th Gen Intel® Core™ Desktop Processors Introducing the New 11th Gen Intel® Core™ Desktop Processors The 11th Gen Intel® Core™ desktop processor family puts you in control of your compute experience. It features an innovative new architecture for reimag- ined performance, immersive display and graphics for incredible visuals, and a range of options and technologies for enhanced tuning. When these advances come together, you have everything you need for fast-paced professional work, elite gaming, inspired creativity, and extreme tuning. The 11th Gen Intel® Core™ desktop processor family gives you the power to perform, compete, excel, and power your greatest contributions. Product Brief 11th Gen Intel® Core™ Desktop Processors PERFORMANCE Reimagined Performance 11th Gen Intel® Core™ desktop processors are intelligently engineered to push the boundaries of performance. The new processor core architecture transforms hardware and software efficiency and takes advantage of Intel® Deep Learning Boost to accelerate AI performance. Key platform improvements include memory support up to DDR4-3200, up to 20 CPU PCIe 4.0 lanes,1 integrated USB 3.2 Gen 2x2 (20G), and Intel® Optane™ memory H20 with SSD support.2 Together, these technologies bring the power and the intelligence you need to supercharge productivity, stay in the creative flow, and game at the highest level. Product Brief 11th Gen Intel® Core™ Desktop Processors Experience rich, stunning, seamless visuals with the high-performance graphics on 11th Gen Intel® Core™ desktop -
Intel® Omni-Path Architecture (Intel® OPA) for Machine Learning
Big Data ® The Intel Omni-Path Architecture (OPA) for Machine Learning Big Data Sponsored by Intel Srini Chari, Ph.D., MBA and M. R. Pamidi Ph.D. December 2017 mailto:[email protected] Executive Summary Machine Learning (ML), a major component of Artificial Intelligence (AI), is rapidly evolving and significantly improving growth, profits and operational efficiencies in virtually every industry. This is being driven – in large part – by continuing improvements in High Performance Computing (HPC) systems and related innovations in software and algorithms to harness these HPC systems. However, there are several barriers to implement Machine Learning (particularly Deep Learning – DL, a subset of ML) at scale: • It is hard for HPC systems to perform and scale to handle the massive growth of the volume, velocity and variety of data that must be processed. • Implementing DL requires deploying several technologies: applications, frameworks, libraries, development tools and reliable HPC processors, fabrics and storage. This is www.cabotpartners.com hard, laborious and very time-consuming. • Training followed by Inference are two separate ML steps. Training traditionally took days/weeks, whereas Inference was near real-time. Increasingly, to make more accurate inferences, faster re-Training on new data is required. So, Training must now be done in a few hours. This requires novel parallel computing methods and large-scale high- performance systems/fabrics. To help clients overcome these barriers and unleash AI/ML innovation, Intel provides a comprehensive ML solution stack with multiple technology options. Intel’s pioneering research in parallel ML algorithms and the Intel® Omni-Path Architecture (OPA) fabric minimize communications overhead and improve ML computational efficiency at scale.