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Download Speeds Performance and Optimized for Long Battery Life
® QUALCOMM FEATURING THE LATEST IN MOBILE TECHNOLOGY. TM SNAPDRAGON Capture sharper, higher-quality images, in challenging lighting situations Qualcomm’s Spectra 14-bit dual image signal processors tap into the enhanced performance and feature enhancements that Hexagon 680 DSP’s HVX 820MOBILE PROCESSOR performance adds with amazing features like Low Light Photo and Video and Touch-to-Track where ISP and DSP work intelligently together to enhance imaging as well as track movements and improve zoom. Enabling a more immersive, intuitive and Immersive, life-like connected experience. virtual reality The Snapdragon 820 mobile processor Experience realistic, visual and audio immersion and offers many advantages: smooth VR action enabled by Snapdragon 820’s • New X12 LTE: Industry leading Heterogeneous compute platform, designed for high connectivity with LTE download speeds performance and optimized for long battery life. of up to 600 Mbps and multi-gigabit 802.11ad Wi-Fi • New Qualcomm® Kryo CPU: Delivering Next-generation maximum performance and low power consumption Kryo is QTI’s first custom computer vision 64-bit quad-core CPU, manufactured Drive more safely with object detection and enhanced in advanced 14nm FinFET LPP process navigation and enhance your smartphone camera • New Qualcomm® Adreno 530: Up to capability with features that can track faces and 40% better graphics and compute objects for a more intelligent mobile experience. performance with the Adreno 530 GPU • Qualcomm Spectra™ 14-bit dual image signal processors (ISPs) deliver high Deeply immersive resolution DSLR-quality images using heterogeneous compute for advanced 3D gaming processing and additional power The combination of Snapdragon 820’s Adreno 530 GPU savings and Kryo CPU creates enough compute performance • New Hexagon 680 DSP includes to enable console quality games and exciting, next Hexagon Vector eXtensions and Sensor generation virtual reality applications. -
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 -
GPU Developments 2018
GPU Developments 2018 2018 GPU Developments 2018 © Copyright Jon Peddie Research 2019. All rights reserved. Reproduction in whole or in part is prohibited without written permission from Jon Peddie Research. This report is the property of Jon Peddie Research (JPR) and made available to a restricted number of clients only upon these terms and conditions. Agreement not to copy or disclose. This report and all future reports or other materials provided by JPR pursuant to this subscription (collectively, “Reports”) are protected by: (i) federal copyright, pursuant to the Copyright Act of 1976; and (ii) the nondisclosure provisions set forth immediately following. License, exclusive use, and agreement not to disclose. Reports are the trade secret property exclusively of JPR and are made available to a restricted number of clients, for their exclusive use and only upon the following terms and conditions. JPR grants site-wide license to read and utilize the information in the Reports, exclusively to the initial subscriber to the Reports, its subsidiaries, divisions, and employees (collectively, “Subscriber”). The Reports shall, at all times, be treated by Subscriber as proprietary and confidential documents, for internal use only. Subscriber agrees that it will not reproduce for or share any of the material in the Reports (“Material”) with any entity or individual other than Subscriber (“Shared Third Party”) (collectively, “Share” or “Sharing”), without the advance written permission of JPR. Subscriber shall be liable for any breach of this agreement and shall be subject to cancellation of its subscription to Reports. Without limiting this liability, Subscriber shall be liable for any damages suffered by JPR as a result of any Sharing of any Material, without advance written permission of JPR. -
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, -
Penguin Computing Upgrades Corona with Latest AMD Radeon Instinct GPU Technology for Enhanced ML and AI Capabilities
Penguin Computing Upgrades Corona with latest AMD Radeon Instinct GPU Technology for Enhanced ML and AI Capabilities November 18, 2019 Fremont, CA., November 18, 2019 -Penguin Computing, a leader in high-performance computing (HPC), artificial intelligence (AI), and enterprise data center solutions and services, today announced that Corona, an HPC cluster first delivered to Lawrence Livermore National Lab (LLNL) in late 2018, has been upgraded with the newest AMD Radeon Instinct™ MI60 accelerators, based on Vega which, per AMD, is the World’s 1st 7nm GPU architecture that brings PCIe® 4.0 support. This upgrade is the latest example of Penguin Computing and LLNL’s ongoing collaboration aimed at providing additional capabilities to the LLNL user community. As previously released, the cluster consists of 170 two-socket nodes with 24-core AMD EPYCTM 7401 processors and a PCIe 1.6 Terabyte (TB) nonvolatile (solid-state) memory device. Each Corona compute node is GPU-ready with half of those nodes today utilizing four AMD Radeon Instinct MI25 accelerators per node, delivering 4.2 petaFLOPS of FP32 peak performance. With the MI60 upgrade, the cluster increases its potential PFLOPS peak performance to 9.45 petaFLOPS of FP32 peak performance. This brings significantly greater performance and AI capabilities to the research communities. “The Penguin Computing DOE team continues our collaborative venture with our vendor partners AMD and Mellanox to ensure the Livermore Corona GPU enhancements expand the capabilities to continue their mission outreach within various machine learning communities,” said Ken Gudenrath, Director of Federal Systems at Penguin Computing. Corona is being made available to industry through LLNL’s High Performance Computing Innovation Center (HPCIC). -
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. -
Macbook Pro (15-Inch, 2019) - Technical Specifications
MacBook Pro (15-inch, 2019) - Technical Specifications Touch Bar Touch Bar with integrated Touch ID sensor Finish Silver Space Gray Display Retina display 15.4-inch (diagonal) LED-backlit display with IPS technology; 2880- by-1800 native resolution at 220 pixels per inch with support for millions of colors Supported scaled resolutions: 1920 by 1200 1680 by 1050 1280 by 800 1024 by 640 500 nits brightness Wide color (P3) True Tone technology Processor 2.6GHz 2.6GHz 6-core Intel Core i7, Turbo Boost up to 4.5GHz, with 12MB shared L3 cache Configurable to 2.4GHz 8-core Intel Core i9, Turbo Boost up to 5.0GHz, with 16MB shared L3 cache 2.3GHz 2.3GHz 8-core Intel Core i9, Turbo Boost up to 4.8GHz, with 16MB shared L3 cache Configurable to 2.4GHz 8-core Intel Core i9, Turbo Boost up to 5.0GHz, with 16MB shared L3 cache Storage MacBook Pro (15-inch, 2019) - Technical Specifications 256GB 256GB SSD Configurable to 512GB, 1TB, 2TB, or 4TB SSD 512GB 512GB SSD Configurable to 1TB, 2TB, or 4TB SSD Memory 16GB of 2400MHz DDR4 onboard memory Configurable to 32GB of memory Graphics 2.6GHz Radeon Pro 555X with 4GB of GDDR5 memory and automatic graphics switching Intel UHD Graphics 630 Configurable to Radeon Pro 560X with 4GB of GDDR5 memory 2.3GHz Radeon Pro 560X with 4GB of GDDR5 memory and automatic graphics switching Intel UHD Graphics 630 Configurable to Radeon Pro Vega 16 with 4GB of HBM2 memory or Radeon Pro Vega 20 with 4GB of HBM2 memory Charging and Expansion Four Thunderbolt 3 (USB-C) ports with support for: Charging DisplayPort Thunderbolt -
Apple Products and Pricing List
Follow us on Facebook and Twitter!! www.illutechstore.com www.facebook.com/LLUcomputerstore (909) 558-4129 www.twitter/iLLUTechStore MacBook Pro 13” 1.4GHz Intel i5 quad-core 8th-gen Processor, iMac Touch Bar & ID, 8GB 2133MHz Memory, Intel MacBook Air Iris Plus Graphics 640, 128GB SSD Storage 21.5” 2.3GHz i5 dual–core Processor, MUHN2LL/A or MUHQ2LL/A Education $1199 8GB 2133MHz Memory, 1TB Hard Drive, Intel Iris Plus Graphics 640 13” 1.6GHz i5 dual-core , (New Model) 1.4GHz Intel i5 quad-core 8th-gen Processor, MMQA2LL/A Education $1049 8GB 2133MHz Memory, 128GB SSD Storage, Touch Bar and ID, 8GB 2133MHz Memory, Intel UHD 617 Graphics Intel Iris Plus Graphics 640, 256GB SSD Stor- 21.5” 3.6GHz quad–core Intel core i3, MVFK2LL/A / MVFM2LL/A age Retina 4K Display, (New Model) MVFHLL/A Education $999 MUHP2LL/A or MUHR2LL/A Education $1399 8GB 2666MHz DDR4 Memory, 1TB Hard Drive, Radeon Pro 555X with 2GB Memory 13” 1.6GHz i5 dual-core , (New Model) 2.4GHz quad-core 8th-generation Intel core i5 MRT32LL/A Education $1249 8GB 2133MHz Memory, 256GB SSD Storage, Processor, Touch Bar and Touch ID, Intel UHD 617 Graphics 8GB 2133MHz LPDDR3 Memory, Intel Iris Plus 21.5” 3.0GHz 6-core Intel Core i5 Turbo MVFL2LL/A / MVFN2LL/A Graphics 655, 256GB SSD Storage MVFJ2LL/A Education $1199 MV962LL/A or MV992LL/A Education $1699 Boost up to 4.1GHz, (New Model) Retina 4K Display, 2.4GHz quad-core 8th-generation Intel core i5 8GB 2666MHz Memory, 1TB Fusion Drive, Processor, Touch Bar & ID, Radeon Pro 560X with 4GB Memory 8GB 2133MHz LPDDR3 Memory, Intel Iris -
Survey and Benchmarking of Machine Learning Accelerators
1 Survey and Benchmarking of Machine Learning Accelerators Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, and Jeremy Kepner MIT Lincoln Laboratory Supercomputing Center Lexington, MA, USA freuther,pmichaleas,michael.jones,vijayg,sid,[email protected] Abstract—Advances in multicore processors and accelerators components play a major role in the success or failure of an have opened the flood gates to greater exploration and application AI system. of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore’s Law, have prompted an explosion of processors and accelerators that promise even greater computational and ma- chine learning capabilities. These processors and accelerators are coming in many forms, from CPUs and GPUs to ASICs, FPGAs, and dataflow accelerators. This paper surveys the current state of these processors and accelerators that have been publicly announced with performance and power consumption numbers. The performance and power values are plotted on a scatter graph and a number of dimensions and observations from the trends on this plot are discussed and analyzed. For instance, there are interesting trends in the plot regarding power consumption, numerical precision, and inference versus training. We then select and benchmark two commercially- available low size, weight, and power (SWaP) accelerators as these processors are the most interesting for embedded and Fig. 1. Canonical AI architecture consists of sensors, data conditioning, mobile machine learning inference applications that are most algorithms, modern computing, robust AI, human-machine teaming, and users (missions). Each step is critical in developing end-to-end AI applications and applicable to the DoD and other SWaP constrained users.