UNIVERSITAS GUNADARMA FAKULTAS ILMU KOMPUTER Manajemen Informatika
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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 ............................................................................................................................................................................................................................. -
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. -
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). -
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, -
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, -
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. -
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 -
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. -
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. -
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 . -
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. -
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.