DOCSLIB.ORG

UNIVERSITAS GUNADARMA FAKULTAS ILMU KOMPUTER Manajemen Informatika

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
UNIVERSITAS GUNADARMA FAKULTAS ILMU KOMPUTER Manajemen Informatika UNIVERSITAS GUNADARMA FAKULTAS ILMU KOMPUTER Manajemen Informatika MAKALAH PENGANTAR GRAFIK KOMPUTER NVIDIA Corporation Kelas : 2DB02 Di Susun Oleh : Clara Widhani Fauziah (38116138) Erizka Dwi Dewanty (32116368) Faris Naufal Darmawan (32116671) Lutfi Adnan Firdaus (34116139) Prio Saifulloh (35116799) UNIVERSITAS GUNADARMA 2018 KATA PENGANTAR Dengan menyebut nama Allah SWT yang Maha Pengasih lagi Maha Panyayang, Kami panjatkan puja dan puji syukur atas kehadirat-Nya, yang telah melimpahkan rahmat, hidayah, dan inayah-Nya kepada kami, sehingga kami dapat menyelesaikan makalah ilmiah tentang “NVIDIA”. Makalah ilmiah ini telah kami susun dengan maksimal dan mendapatkan bantuan dari berbagai pihak sehingga dapat memperlancar pembuatan makalah ini. Untuk itu kami menyampaikan banyak terima kasih kepada semua pihak yang telah berkontribusi dalam pembuatan makalah ini serta kepada Dosen Pembimbing kami Ibu Karmilasari yang telah memberikan bimbingan dan tugas dalam makalah ini. Terlepas dari semua itu, Kami menyadari sepenuhnya bahwa masih ada kekurangan baik dari segi susunan kalimat maupun tata bahasanya. Oleh karena itu kami menerima segala saran dan kritik dari pembaca agar kami dapat memperbaiki makalah ilmiah ini. Akhir kata kami berharap semoga makalah ilmiah tentang “NVIDIA” Semoga dengan adanya tugas ini kita dapat belajar bersama demi kemajuan bidang Teknologi Informasi dan kemajuan ilmu pengetahuan Jakarta, 14 Maret 2018 Kelompok 5 ii Daftar Isi Halaman Judul _______________________________________________ i Kata Pengantar _______________________________________________ ii Daftar Isi____________________________________________________ iii BAB I. Pendahuluan A. Latar Belakang_________________________________________ 1 B. Tujuan Penulisan________________________________________ 1 C. Rumusan Masalah_______________________________________ 2 D. Metode Penelitian_______________________________________ 2 E. Sistematika Penulisan____________________________________ 2 BAB II. Pembahasan A. Sejarah Awal Pembentukan nVidia_________________________ 3 B. Produksi nVidia dari tahun ke tahun_________________________ 4 C. Kelebihan dan Kekurangan________________________________ 23 BAB III. Penutup A. Kesimpulan ___________________________________________ 24 B. Saran________________________________________________ 24 Daftar Pustaka ______________________________________________ 25 iii BAB I PENDAHULUAN A. Latar Belakang Saat ini perpaduan antara perkembangan teknologi dan ilmu pengetahuan sudah banyak dilakukan orang khususnya dalam pengolahan citra. Pengolahan citra merupakan proses memperbaiki kualitas citra agar mudah diinterpretasikan oleh manusia atau komputer, salah satu dari operasi pengolahan citra adalah segmentasi. Segmentasi citra merupakan proses pemecahan suatu berdasarkan perubahan kasar dalam intensitas seperti tepi pada citra. Komputasi paralel merupakan salah satu teknik melakukan komputasi secara bersamaan dengan memanfaatkan beberapa komputer independen secara bersamaan. Dasar komputasi paralel adalah membagi pekerjaan ke banyak unit pemroses, sehingga mempercepat pemrosesan pekerjaan tersebut. Unit pemroses yang digunakan adalah GPU (Graphic Processing Unit). Komputasi paralel diimplementasikan dengan CUDA (Compute Unified Device Architecture). CUDA merupakan arsitektur dan model programming dari NVIDIA yang dapat menjalankan GPU (NVIDIA). Dalam hal ini, diperlukan penelitian untuk mempercepat proses pada metode level set untuk active contour B. Tujuan Penulisan Makalah yang disusun bertujuan untuk memenuhi tugas kelompok mata pelajaran Pengantar Grafik Komputer. Selain itu, makalah ini juga disusun untuk menambah wawasan dan ilmu pengetahuan tentang pemahaman perkembangan Grafik Komputer saat ini salah satunya NVIDIA. 1 2 C. Rumusan Masalah 1. Bagaimana Sejarah Perkembangan Berdirinya NVIDIA? 2. Bagaimana proses produksi grafis NVIDIA dari tahun ke tahun? 3. Apa saja kelebihan dan kekurangan nVidia? D. Metode Penelitian Ada sebuah metode yang dilakukan penulis untuk menunjang penulisan ilmiah ini. 1. Pengumpulan Data Penulis melakukan pengumpulan data mengenai Pengantar Grafik Komputer dengan melakukan pencarian melalui browsing internet. E. Sistematika Penulisan Sistematika penulisan bertujuan untuk memberikan gambaran mengenai isi dari bab-bab yang terdapat dalam penulisan ilmiah. Isi keseluruhan penulisan ilmiah ini adalah sebagai berikut : BAB I PENDAHULUAN, Bab ini menjabarkan mengenai latar belakang penulisan ilmiah, rumusan masalah, tujuan penelitian, metode penelitian dan sistematika penulisan yang terkait dengan penelitian yang dilakukan. BAB II PEMBAHASAN, Bab ini menjelaskan tentang pokok bahasan yang kami angkat dengan cara mendetail dan menggunakan bahasa yang komunikatif sehingga dapat mudah diterima bagi para pembaca. BAB III PENUTUP, Bab ini berisi kesimpulan dan saran penulisan ilmiah. BAB II PEMBAHASAN A. Sejarah Awal Pembentukan Nvidia NVIDIA Corporation adalah sebuah perusahaan produsen processor grafis (graphics processing unit), kartu grafis, dan media serta alat-alat komunikasi untuk komputer pribadi, dan konsol permainan Sonny Playstation 3. Produk paling terkenal dari NVIDIA adalah seri GeForce yang digunakan untuk bermain permainan di komputer. Markas utama NVIDIA berada di Jalan Bebas Hambatan San Tomas, Santa Clara, California. Berdirinya nvidia pada tahun 1993 yang di gagas oleh beberapa pendirinya , antara lain adalah Jensen Huang ( CEO nvidia yang sekarang ) , Curtis Priem dan Chris malachowsky, ide nama nvidia sebagai nama perusahaan digagas dari kombinasi antara huruf inisial n sebagai variabel dalam matematika - dan kata video berasal dari bahasa latin yang artinya melihat hingga akhirnya nama tersebut memiliki makna "pengalaman visual terbaik" Namun dalam publikasi dan dokumentasi perusahaan , nama nvidia lebih sering ditulis dalam huruf kapital NVIDIA. 3 4 B. Produksi NVIDIA dari Tahun ke Tahun NVIDIA telah memproduksi dari tahun ke tahun menjadi teknologi yang semakin baik dari mulai spesifikasi bahkan model dan lain-lain. Berikut sejarah produksi grafis NVIDIA dari tahun ke tahun: 1. NVIDIA NV1 NVIDIA NV1 yang dirilis tahun 1995 adalah generasi pertama VGA Card yang dikeluarkan oleh NVIDIA. Di dalam NVIDIA NV1 sudah terdapat 2D card, 3D accelerator, sound card, dan sebuah port untuk Sega Saturn Game Controller. Gambar yang dihasilkan : 5 2. NVIDIA RIFA VGA ini adalah buatan pertama dari Nvidia, vga ini juga dibuat dengan desain teknologi Microsoft's DirectX 5 API. NVIDIA NV3 memiliki spec. 4 MB memory, 100 MHz core clockspeeds, bandwith 1.6 GB/s, 206 MHz RAMDAC dan mendukung AGP 2x. Spesifikasi: 128-bit Board 350nm Fabrication 100MHz Core BMB Memory On Clockspeed Gambar yang dihasilkan : 6 3. NVIDIA NV4 VGA ini merupaka penerus dari NVIDIA NV3, namun NVIDIA NV4 tidak ada penambahan yamh signifikan dalam spec. seperti memory maksimum ditambah menjadi 16 MB dan mempunyai clockspeeds pada 110 MHz, tetapi Nvidia manambahkan beberapa kemampuan pada NVIDIA NV4 seperti teknologi “second pixel pipeline”, 32-bit true colors, dan fitur Trilinear filtering. Spesifikasi : 128-bit Memory On Board 350nm Fabrication 90 MHz Core 8MB & 16 MB Clockspeed Gambar yang dihasilkan : 7 4. NVIDIA RIVA TNT 1 & 2 RIVA merupakan kepanjangan dari Realtime interactive Video and Animation Accelarator dan TNT adalah singkatan dari Twin Texel, TNT pertama kali dirilis pada tahun pada akhir tahun 1998. TNT2 pada awal 1999, keduanya sebenrnya sangat mirip baik dari kecepatan dan fitur yang di tawarkan, namun TNT mempunyai sedikit perbedaan yaitu telah mendukung AGP4x, mendukung memory hingga 32MB, dan proses produksinya sudah 25PM. Persaingan kartu grafis ini adalah dengan 3DFX VooDoo 2 dan 3 , matroxG400 dan Ati rage 128 . TNT RIVA merupakan kartu grafis pertama yang 100% diciptakan oleh nvidia corporation. Gambar yang dihasilkan : 8 5. NVIDIA GEFORCE 256 VGA Nvidia ini memiliki kecepatan performa 2 kali lebih cepat daripada seri-seri sebelumnya, vga ini memiliki spec. DirectX 7, 4-pixel Rendering pipeline dan sebuah fitur bernama “cube environment mapping” yaitu yang gunanya untuk menciptakan efek real time reflection. Spesifikasi : 128-bit Memory On Board 220nm Fabrication 120MHz Core 32 MB & 64 MB Clockspeed Gambar yang dihasilkan : 9 6. NVIDIA GEFORCE SERIES 2 Ini merupakan VGA Nvidia pertama yang menghadirkan fitur baru, yaitu pixel shader dengan sebutan “Nvidia Shading Rasterizer (NZR)”. Spesifikasi : 180nm Fabrication 64 MB Memory On Board 250MHz Core Clockspeed Gambar yang dihasilkan : 10 7. NVIDIA GEFORCE 3 SERIES GeForce 3 muncul pada tahun 2001. Graphic card pertama yang compatible dengan DirectX 8 dan s mendukung pixels shaders. Graphic card ini juga lebih baik dalam memoey management, tetapi arsitekturnya sangat kompleks sehingga membuat NVIDIA tidak melanjutkan pengembangannya. 8. NVIDIA GEFORCE 4 SERIES NVIDIA geforce 4 ini juga merupakan salah satu seri tersukses dari semua Geforce yang ada. Membawa dua varian untuk dua pangsa pasar yang berbeda menjadikan card ini mempunyai pilihan bagi jenis konsumen, kalangan enthusiast mendapatkan versi Ti dan kalangan yang mementingkan budget mendapatkan versi MX, seperti layaknya kartu sukses, Geforce 4 mempunyai jenis kartu yang cukup laris manis di pasaran, MX440 dan Ti 4200 11 Spekfikasi: Vertices per Second: 136 Million Fill Rate: 4.8 Billion AA Samples/Sec. Operations per Second: 1.23 Trillion Memory Bandwidth: 10.4GB/Sec. Maximum Memory: 128MB Gambar yang dihasilkan : 12 9. NVIDIA GEFORCE FX series NVIDIA kembali merilis graphic card terbarunya
Load more

Recommended publications
  • Gs-35F-4677G
    March 2013 NCS Technologies, Inc. Information Technology (IT) Schedule Contract Number: GS-35F-4677G FEDERAL ACQUISTIION SERVICE INFORMATION TECHNOLOGY SCHEDULE PRICELIST GENERAL PURPOSE COMMERCIAL INFORMATION TECHNOLOGY EQUIPMENT Special Item No. 132-8 Purchase of Hardware 132-8 PURCHASE OF EQUIPMENT FSC CLASS 7010 – SYSTEM CONFIGURATION 1. End User Computer / Desktop 2. Professional Workstation 3. Server 4. Laptop / Portable / Notebook FSC CLASS 7-25 – INPUT/OUTPUT AND STORAGE DEVICES 1. Display 2. Network Equipment 3. Storage Devices including Magnetic Storage, Magnetic Tape and Optical Disk NCS TECHNOLOGIES, INC. 7669 Limestone Drive Gainesville, VA 20155-4038 Tel: (703) 621-1700 Fax: (703) 621-1701 Website: www.ncst.com Contract Number: GS-35F-4677G – Option Year 3 Period Covered by Contract: May 15, 1997 through May 14, 2017 GENERAL SERVICE ADMINISTRATION FEDERAL ACQUISTIION SERVICE Products and ordering information in this Authorized FAS IT Schedule Price List is also available on the GSA Advantage! System. Agencies can browse GSA Advantage! By accessing GSA’s Home Page via Internet at www.gsa.gov. TABLE OF CONTENTS INFORMATION FOR ORDERING OFFICES ............................................................................................................................................................................................................................... TC-1 SPECIAL NOTICE TO AGENCIES – SMALL BUSINESS PARTICIPATION 1. Geographical Scope of Contract .............................................................................................................................................................................................................................
    [Show full text]
  • NVIDIA® Geforce® 7300 GT Gpus Features and Benefits
    NVIDIA GEFORCE 7 SERIES MARKETING MATERIALS NVIDIA® GeForce® 7300 GT GPUs Features and Benefits Next-Generation Superscalar GPU Architecture Delivers over 2x the shading power of previous generation products taking gaming performance to extreme levels. Full Microsoft® DirectX® 9.0 Shader Model 3.0 Support The standard for today’s PCs and next-generation consoles enables stunning and complex effects for cinematic realism. NVIDIA GPUs offer the most complete implementation of the Shader Model 3.0 feature set—including vertex texture fetch (VTF)—to ensure top-notch compatibility and performance for all DirectX 9 applications. NVIDIA® CineFX® 4.0 Engine Delivers advanced visual effects at unimaginable speeds. Full support for Microsoft® DirectX® 9.0 Shader Model 3.0 enables stunning and complex special effects. Next-generation shader architecture with new texture unit design streamlines texture processing for faster and smoother gameplay. NVIDIA® SLI™ Technology1 Delivers up to 2x the performance of a single GPU configuration for unparalleled gaming experiences by allowing two graphics cards to run in parallel. The must-have feature for performance PCI Express® graphics, SLI dramatically scales performance on today’s hottest games. NVIDIA® Intellisample™ 4.0 Technology The industry’s fastest antialiasing delivers ultra-realistic visuals, with no jagged edges, at lightning- fast speeds. Visual quality is taken to new heights through a new rotated grid sampling pattern, advanced 128 tap sample coverage, 16x anisotropic filtering, and support for transparent supersampling and multisampling. True High Dynamic-Range (HDR) Rendering Support The ultimate lighting effects bring environments to life for a truly immersive, ultra-reailstic experience. Based on the OpenEXR technology from Industrial Light & Magic (http://www.openexr.com/), NVIDIA’s 64-bit texture implementation delivers state-of-the-art high dynamic-range (HDR) visual effects through floating point capabilities in shading, filtering, texturing, and blending.
    [Show full text]
  • Fast Calculation of the Lomb-Scargle Periodogram Using Graphics Processing Units 3
    Draft version August 7, 2018 A Preprint typeset using LTEX style emulateapj v. 11/10/09 FAST CALCULATION OF THE LOMB-SCARGLE PERIODOGRAM USING GRAPHICS PROCESSING UNITS R. H. D. Townsend Department of Astronomy, University of Wisconsin-Madison, Sterling Hall, 475 N. Charter Street, Madison, WI 53706, USA; [email protected] Draft version August 7, 2018 ABSTRACT I introduce a new code for fast calculation of the Lomb-Scargle periodogram, that leverages the computing power of graphics processing units (GPUs). After establishing a background to the newly emergent field of GPU computing, I discuss the code design and narrate key parts of its source. Benchmarking calculations indicate no significant differences in accuracy compared to an equivalent CPU-based code. However, the differences in performance are pronounced; running on a low-end GPU, the code can match 8 CPU cores, and on a high-end GPU it is faster by a factor approaching thirty. Applications of the code include analysis of long photometric time series obtained by ongoing satellite missions and upcoming ground-based monitoring facilities; and Monte-Carlo simulation of periodogram statistical properties. Subject headings: methods: data analysis — methods: numerical — techniques: photometric — stars: oscillations 1. INTRODUCTION of the newly emergent field of GPU computing; then, Astronomical time-series observations are often charac- Section 3 reviews the formalism defining the L-S peri- terized by uneven temporal sampling (e.g., due to trans- odogram, and Section 4 presents a GPU-based code im- formation to the heliocentric frame) and/or non-uniform plementing this formalism. Benchmarking calculations coverage (e.g., from day/night cycles, or radiation belt to evaluate the accuracy and performance of the code passages).
    [Show full text]
  • Nvidia Forceware Graphics Drivers for XP, Manual and Notes
    ForceWare Graphics Drivers Release 162 Notes Version 162.18 for Windows XP NVIDIA Corporation June 29, 2007 Confidential Information Published by NVIDIA Corporation 2701 San Tomas Expressway Santa Clara, CA 95050 Notice ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, “MATERIALS”) ARE BEING PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication or otherwise under any patent or patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all information previously supplied. NVIDIA Corporation products are not authorized for use as critical components in life support devices or systems without express written approval of NVIDIA Corporation. Trademarks NVIDIA, the NVIDIA logo, 3DFX, 3DFX INTERACTIVE, the 3dfx Logo, STB, STB Systems and Design, the STB Logo, the StarBox Logo, NVIDIA nForce, GeForce, NVIDIA Quadro, NVDVD, NVIDIA Personal Cinema, NVIDIA Soundstorm, Vanta, TNT2, TNT,
    [Show full text]
  • Release 85 Notes
    ForceWare Graphics Drivers Release 85 Notes Version 88.61 For Windows Vista x86 and Windows Vista x64 NVIDIA Corporation May 2006 Published by NVIDIA Corporation 2701 San Tomas Expressway Santa Clara, CA 95050 Notice ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, “MATERIALS”) ARE BEING PROVIDED “AS IS.” NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE. Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication or otherwise under any patent or patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all information previously supplied. NVIDIA Corporation products are not authorized for use as critical components in life support devices or systems without express written approval of NVIDIA Corporation. Trademarks NVIDIA, the NVIDIA logo, 3DFX, 3DFX INTERACTIVE, the 3dfx Logo, STB, STB Systems and Design, the STB Logo, the StarBox Logo, NVIDIA nForce, GeForce, NVIDIA Quadro, NVDVD, NVIDIA Personal Cinema, NVIDIA Soundstorm, Vanta, TNT2, TNT,
    [Show full text]
  • True Random Number Generation Using Genetic Algorithms on High Performance Architectures
    True Random Number Generation using Genetic Algorithms on High Performance Architectures by Jose Juan Mijares Chan A thesis submitted to The Faculty of Graduate Studies of The University of Manitoba in partial fulfillment of the requirements of the degree of Doctor of Philosophy Department of Electrical and Computer Engineering The University of Manitoba Winnipeg, Manitoba, Canada October 2016 © Copyright 2016 by Jose Juan Mijares Chan Thesis advisor Author Gabriel Thomas, Parimala Thulasiraman Jose Juan Mijares Chan True Random Number Generation using Genetic Algorithms on High Performance Architectures Abstract Many real-world applications use random numbers generated by pseudo-random number and true random number generators (TRNG). Unlike pseudo-random number generators which rely on an input seed to generate random numbers, a TRNG relies on a non-deterministic source to generate aperiodic random numbers. In this research, we develop a novel and generic software-based TRNG using a random source extracted from compute architectures of today. We show that the non-deterministic events such as race conditions between compute threads follow a near Gamma distribution, independent of the architecture, multi-cores or co-processors. Our design improves the distribution towards a uniform distribution ensuring the stationarity of the sequence of random variables. We improve the random numbers statistical deficiencies by using a post-processing stage based on a heuristic evolutionary algorithm. Our post-processing algorithm is composed of two phases: (i) Histogram Specification and (ii) Stationarity Enforcement. We propose two techniques for histogram equalization, Exact Histogram Equalization (EHE) and Adaptive EHE (AEHE) that maps the random numbers distribution to ii Abstract iii a user-specified distribution.
    [Show full text]
  • Manycore GPU Architectures and Programming, Part 1
    Lecture 19: Manycore GPU Architectures and Programming, Part 1 Concurrent and Mul=core Programming CSE 436/536, [email protected] www.secs.oakland.edu/~yan 1 Topics (Part 2) • Parallel architectures and hardware – Parallel computer architectures – Memory hierarchy and cache coherency • Manycore GPU architectures and programming – GPUs architectures – CUDA programming – Introduc?on to offloading model in OpenMP and OpenACC • Programming on large scale systems (Chapter 6) – MPI (point to point and collec=ves) – Introduc?on to PGAS languages, UPC and Chapel • Parallel algorithms (Chapter 8,9 &10) – Dense matrix, and sorng 2 Manycore GPU Architectures and Programming: Outline • Introduc?on – GPU architectures, GPGPUs, and CUDA • GPU Execuon model • CUDA Programming model • Working with Memory in CUDA – Global memory, shared and constant memory • Streams and concurrency • CUDA instruc?on intrinsic and library • Performance, profiling, debugging, and error handling • Direc?ve-based high-level programming model – OpenACC and OpenMP 3 Computer Graphics GPU: Graphics Processing Unit 4 Graphics Processing Unit (GPU) Image: h[p://www.ntu.edu.sg/home/ehchua/programming/opengl/CG_BasicsTheory.html 5 Graphics Processing Unit (GPU) • Enriching user visual experience • Delivering energy-efficient compung • Unlocking poten?als of complex apps • Enabling Deeper scien?fic discovery 6 What is GPU Today? • It is a processor op?mized for 2D/3D graphics, video, visual compu?ng, and display. • It is highly parallel, highly multhreaded mulprocessor op?mized for visual
    [Show full text]
  • NVIDIA® Geforce® 7900 Gpus Features and Benefits Next
    NVIDIA GEFORCE 7 SERIES MARKETING MATERIALS NVIDIA® GeForce® 7900 GPUs Features and Benefits Next-Generation Superscalar GPU Architecture: Delivers over 2x the shading power of previous generation products taking gaming performance to extreme levels. Full Microsoft® DirectX® 9.0 Shader Model 3.0 Support: The standard for today’s PCs and next-generation consoles enables stunning and complex effects for cinematic realism. NVIDIA GPUs offer the most complete implementation of the Shader Model 3.0 feature set—including vertex texture fetch (VTF)—to ensure top-notch compatibility and performance for all DirectX 9 applications. NVIDIA® CineFX® 4.0 Engine: Delivers advanced visual effects at unimaginable speeds. Full support for Microsoft® DirectX® 9.0 Shader Model 3.0 enables stunning and complex special effects. Next-generation shader architecture with new texture unit design streamlines texture processing for faster and smoother gameplay. NVIDIA® SLI™ Technology*: Delivers up to 2x the performance of a single GPU configuration for unparalleled gaming experiences by allowing two graphics cards to run in parallel. The must-have feature for performance PCI Express® graphics, SLI dramatically scales performance on today’s hottest games. NVIDIA® Intellisample™ 4.0 Technology: The industry’s fastest antialiasing delivers ultra- realistic visuals, with no jagged edges, at lightning-fast speeds. Visual quality is taken to new heights through a new rotated grid sampling pattern, advanced 128 tap sample coverage, 16x anisotropic filtering, and support for transparent supersampling and multisampling. True High Dynamic-Range (HDR) Rendering Support: The ultimate lighting effects bring environments to life for a truly immersive, ultra-realistic experience. Based on the OpenEXR technology from Industrial Light & Magic (http://www.openexr.com/), NVIDIA’s 64-bit texture implementation delivers state-of-the-art high dynamic-range (HDR) visual effects through floating point capabilities in shading, filtering, texturing, and blending.
    [Show full text]
  • Nvidia Geforce Go 7400 Driver Download Windows 7
    Nvidia geforce go 7400 driver download windows 7 click here to download Operating System, Windows Vista bit NVIDIA recommends that you check with your notebook OEM about recommended Before downloading this driver: Beta driver for GeForce Go 7-series, GeForce 8M and GeForce 9M series Go , GeForce Go , GeForce Go , GeForce Go Operating System: Windows Vista bit, Windows 7 bit Beta driver for GeForce Go 7-series, GeForce 8M and GeForce 9M series notebook GPUs. in several free applications and demos by downloading the GeForce Plus Pack. GTX, GeForce Go , GeForce Go , GeForce Go , GeForce Go , . Since all GeForce Go 7 Series GPUs are designed to be compatible with the next -generation Microsoft® Windows Vista™ operating system (OS), you can rest. Download nVidia GeForce Go video card drivers or install DriverPack Solution software Operating System Versions: Windows XP, 7, 8, , 10 (x64, x86). This package supports the following driver models:NVIDIA GeForce Free Sony Windows NT//XP/ Version Full Specs. Free NVIDIA Windows 98/NT//XP/ Version Full Specs NVIDIA GeForce Go and NVIDIA GeForce Go graphics processing units ( GPUs) deliver all of of the award-winning GeForce 7 Series GPU architecture to thin and light notebook PCs. Subcategory, Keyboard Drivers. Download the latest Nvidia GeForce Go device drivers (Official and Certified). Nvidia GeForce Go Compatibility: Windows XP, Vista, 7, 8, Downloads. nVidia - Display - NVIDIA GeForce Go , Windows XP Bit Edition Version , Drivers (Other Hardware), 4/3/, , MB NVIDIA GeForce Go - Driver Download. Updating your drivers with Driver Alert can help your computer in a number of ways. Windows 7 Bit Driver.
    [Show full text]
  • CPU-GPU Benchmark Description
    UC San Diego UC San Diego Electronic Theses and Dissertations Title Heterogeneity and Density Aware Design of Computing Systems Permalink https://escholarship.org/uc/item/9qd6w8cw Author Arora, Manish Publication Date 2018 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Heterogeneity and Density Aware Design of Computing Systems A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science (Computer Engineering) by Manish Arora Committee in charge: Professor Dean M. Tullsen, Chair Professor Renkun Chen Professor George Porter Professor Tajana Simunic Rosing Professor Steve Swanson 2018 Copyright Manish Arora, 2018 All rights reserved. The dissertation of Manish Arora is approved, and it is ac- ceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2018 iii DEDICATION To my family and friends. iv EPIGRAPH Do not be embarrassed by your failures, learn from them and start again. —Richard Branson If something’s important enough, you should try. Even if the probable outcome is failure. —Elon Musk v TABLE OF CONTENTS Signature Page . iii Dedication . iv Epigraph . v Table of Contents . vi List of Figures . viii List of Tables . x Acknowledgements . xi Vita . xiii Abstract of the Dissertation . xvi Chapter 1 Introduction . 1 1.1 Design for Heterogeneity . 2 1.1.1 Heterogeneity Aware Design . 3 1.2 Design for Density . 6 1.2.1 Density Aware Design . 7 1.3 Overview of Dissertation . 8 Chapter 2 Background . 10 2.1 GPGPU Computing . 10 2.2 CPU-GPU Systems .
    [Show full text]
  • High-Performance and Energy-Efficient Irregular Graph Processing on GPU Architectures
    High-Performance and Energy-Efficient Irregular Graph Processing on GPU architectures Albert Segura Salvador Doctor of Philosophy Department of Computer Architecture Universitat Politècnica de Catalunya Advisors: Jose-Maria Arnau Antonio González July, 2020 2 Abstract Graph processing is an established and prominent domain that is the foundation of new emerging applications in areas such as Data Analytics, Big Data and Machine Learning. Appli- cations such as road navigational systems, recommendation systems, social networks, Automatic Speech Recognition (ASR) and many others are illustrative cases of graph-based datasets and workloads. Demand for higher processing of large graph-based workloads is expected to rise due to nowadays trends towards increased data generation and gathering, higher inter-connectivity and inter-linkage, and in general a further knowledge-based society. An increased demand that poses challenges to current and future graph processing architectures. To effectively perform graph processing, the large amount of data employed in these domains requires high throughput architectures such as GPGPU. Although the processing of large graph-based workloads exhibits a high degree of parallelism, the memory access patterns tend to be highly irregular, leading to poor GPGPU efficiency due to memory divergence. Graph datasets are sparse, highly unpredictable and unstructured which causes the irregular access patterns and low computation per data ratio, further lowering GPU utilization. The purpose of this thesis is to characterize the bottlenecks and limitations of irregular graph processing on GPGPU architectures in order to propose architectural improvements and extensions that deliver improved performance, energy efficiency and overall increased GPGPU efficiency and utilization. In order to ameliorate these issues, GPGPU graph applications perform stream compaction operations which process the subset of active nodes/edges so subsequent steps work on compacted dataset.
    [Show full text]
  • GPU Accelerated Approach to Numerical Linear Algebra and Matrix Analysis with CFD Applications
    University of Central Florida STARS HIM 1990-2015 2014 GPU Accelerated Approach to Numerical Linear Algebra and Matrix Analysis with CFD Applications Adam Phillips University of Central Florida Part of the Mathematics Commons Find similar works at: https://stars.library.ucf.edu/honorstheses1990-2015 University of Central Florida Libraries http://library.ucf.edu This Open Access is brought to you for free and open access by STARS. It has been accepted for inclusion in HIM 1990-2015 by an authorized administrator of STARS. For more information, please contact [email protected]. Recommended Citation Phillips, Adam, "GPU Accelerated Approach to Numerical Linear Algebra and Matrix Analysis with CFD Applications" (2014). HIM 1990-2015. 1613. https://stars.library.ucf.edu/honorstheses1990-2015/1613 GPU ACCELERATED APPROACH TO NUMERICAL LINEAR ALGEBRA AND MATRIX ANALYSIS WITH CFD APPLICATIONS by ADAM D. PHILLIPS A thesis submitted in partial fulfilment of the requirements for the Honors in the Major Program in Mathematics in the College of Sciences and in The Burnett Honors College at the University of Central Florida Orlando, Florida Spring Term 2014 Thesis Chair: Dr. Bhimsen Shivamoggi c 2014 Adam D. Phillips All Rights Reserved ii ABSTRACT A GPU accelerated approach to numerical linear algebra and matrix analysis with CFD applica- tions is presented. The works objectives are to (1) develop stable and efficient algorithms utilizing multiple NVIDIA GPUs with CUDA to accelerate common matrix computations, (2) optimize these algorithms through CPU/GPU memory allocation, GPU kernel development, CPU/GPU communication, data transfer and bandwidth control to (3) develop parallel CFD applications for Navier Stokes and Lattice Boltzmann analysis methods.
    [Show full text]