GPU-BASED TUNING OF QUANTUM-INSPIRED GENETIC ALGORITHM FOR A COMBINATORIAL OPTIMIZATION PROBLEM
|
|
- Gloria Harrington
- 8 years ago
- Views:
Transcription
1 GPU-BASED TUNING OF QUANTUM-INSPIRED GENETIC ALGORITHM FOR A COMBINATORIAL OPTIMIZATION PROBLEM Robert Nowotniak, Jacek Kucharski Computer Engineering Department The Faculty of Electrical, Electronic, Computer and Control Engineering Technical University of Lodz XIV INTERNATIONAL CONFERENCE SYSTEM MODELLING and CONTROL June 27-29, 2011 Łódź Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
2 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
3 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
4 QUANTUM-INSPIRED GENETIC ALGORITHMS Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
5 QUANTUM-INSPIRED GENETIC ALGORITHMS Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
6 QUANTUM ELEMENTS IN EVOLUTIONARY ALGORITHMS 1 Representation of solutions Instead of exact points in a search space, probability distributions of sampling the space 2 Initialization 3 Genetic operators 4 Evaluation Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
7 QUANTUM ELEMENTS IN EVOLUTIONARY ALGORITHMS 1 Representation of solutions (bits qubits) Instead of exact points in a search space, probability distributions of sampling the space 2 Initialization 3 Genetic operators 4 Evaluation Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
8 QUANTUM ELEMENTS IN EVOLUTIONARY ALGORITHMS 1 Representation of solutions (bits qubits) Instead of exact points in a search space, probability distributions of sampling the space 2 Initialization 3 Genetic operators 4 Evaluation Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
9 CLASSICAL BITS VS QUBITS Geometrical representation of Qubit on the Bloch sphere Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
10 CLASSICAL BITS VS QUBITS Geometrical representation of Qubit on the Bloch sphere Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
11 CLASSICAL BITS VS QUBITS Geometrical representation of Qubit on the Bloch sphere Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
12 QUBITS AND BINARY QUANTUM GENES qubit (quantum bit): ψ = α 0 + β 1 where: α, β C, α 2 + β 2 = 1 Pr ψ : F {0,1} [0, 1] Pr ψ ({0}) = α 2 Pr ψ ({1}) = β 2 1 ψ = }{{} 2 }{{} 2 α β β α ψ 0 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
13 QUBITS AND BINARY QUANTUM GENES qubit (quantum bit): ψ = α 0 + β 1 where: α, β C, α 2 + β 2 = 1 Pr ψ : F {0,1} [0, 1] Pr ψ ({0}) = α 2 Pr ψ ({1}) = β 2 1 ψ = }{{} 2 }{{} 2 α β β α ψ 0 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
14 QUBITS AND BINARY QUANTUM GENES qubit (quantum bit): ψ = α 0 + β 1 where: α, β C, α 2 + β 2 = 1 Pr ψ : F {0,1} [0, 1] Pr ψ ({0}) = α 2 Pr ψ ({1}) = β 2 ψ = }{{} 3 } {{ 3 } α β β 1 α ψ 0 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
15 QUBITS AND BINARY QUANTUM GENES qubit (quantum bit): ψ = α 0 + β 1 where: α, β C, α 2 + β 2 = 1 Pr ψ : F {0,1} [0, 1] Pr ψ ({0}) = α 2 Pr ψ ({1}) = β 2 ψ = }{{} }{{} 1 1 α β β α 1 ψ 0 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
16 SIMPLE GENETIC ALGORITHM In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: population of solutions binary gene chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
17 SIMPLE GENETIC ALGORITHM In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: population of solutions binary gene chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
18 SIMPLE GENETIC ALGORITHM In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: population of solutions binary gene chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
19 SIMPLE GENETIC ALGORITHM In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: population of solutions binary gene chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
20 SIMPLE GENETIC ALGORITHM In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: population of solutions binary gene chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
21 SIMPLE GENETIC ALGORITHM In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: population of solutions binary gene chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
22 SIMPLE GENETIC ALGORITHM In Simple Genetic Algorithm, solutions to technical problems are encoded as binary strings, for example: population of solutions binary gene chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
23 QUANTUM-INSPIRED GENETIC ALGORITHMS In Quantum-Inspired Genetic Algorithms, each individual encodes probability distribution of sampling the search space quantum population quantum gene quantum chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
24 QUANTUM-INSPIRED GENETIC ALGORITHMS In Quantum-Inspired Genetic Algorithms, each individual encodes probability distribution of sampling the search space quantum population quantum gene quantum chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
25 QUANTUM-INSPIRED GENETIC ALGORITHMS In Quantum-Inspired Genetic Algorithms, each individual encodes probability distribution of sampling the search space quantum population quantum gene quantum chromosome Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
26 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
27 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
28 NVIDIA R TESLA TM S Tesla TM s consists of: 4 CUDA GPU cards, each: 30 streaming multiprocessors (SMs) 8 cores each (separate ALUs) 16 KB of shared memory highly effective (zero-overhead) tasks scheduler 4 GB global memory Total: = 960 processor cores Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
29 NVIDIA R TESLA TM S Tesla TM s consists of: 4 CUDA GPU cards, each: 30 streaming multiprocessors (SMs) 8 cores each (separate ALUs) 16 KB of shared memory highly effective (zero-overhead) tasks scheduler 4 GB global memory Total: = 960 processor cores Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
30 THREAD HIERARCHY ON CUDA TM GPU In CUDA, threads are grouped in blocks and blocks constitute a grid. The unit of thread scheduling is warp (32 threads). Grid of Thread Blocks Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
31 PROPOSED APPROACH TO PARALLELIZATION Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
32 GPU-BASED IMPLEMENTATION OF QIGA Two levels: 1 Coarse-grained parallelization In a grid, there can be several hundred blocks evolving independent populations with same or different parameters simultaneously. 2 Fine-grained parallelization On the population level, each individual can be evaluated and transformed in a separate GPU thread. Thus, the whole population can be represented as a block of threads. Hundreds of populations with same or different parameters can be evolved in parallel, simultaneously Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
33 GPU-BASED IMPLEMENTATION OF QIGA Two levels: 1 Coarse-grained parallelization In a grid, there can be several hundred blocks evolving independent populations with same or different parameters simultaneously. 2 Fine-grained parallelization On the population level, each individual can be evaluated and transformed in a separate GPU thread. Thus, the whole population can be represented as a block of threads. Hundreds of populations with same or different parameters can be evolved in parallel, simultaneously Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
34 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
35 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
36 NUMERICAL EXPERIMENT TEST: COMBINATORIAL OPTIMIZATION Knapsack problem (hard version, strongly correlated set of items) Number of items: 250 Comparison: 1 Simple Genetic Algorithm (SGA) popsize = 100, P c = 0.65, P m = Quantum-Inspired Genetic Algorithm (QIGA) popsize = 10, other parameters (rotation angles) as in [ 1 ] 3 Tuned Quantum-Inpsired Genetic Algorithm 1 Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
37 NUMERICAL EXPERIMENT TEST: COMBINATORIAL OPTIMIZATION Knapsack problem (hard version, strongly correlated set of items) Number of items: 250 Comparison: 1 Simple Genetic Algorithm (SGA) popsize = 100, P c = 0.65, P m = Quantum-Inspired Genetic Algorithm (QIGA) popsize = 10, other parameters (rotation angles) as in [ 1 ] 3 Tuned Quantum-Inpsired Genetic Algorithm 1 Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
38 QIGA EXECUTION TIME COMPARISON Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
39 QIGA EXECUTION TIME COMPARISON Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
40 SPEEDUP ON CUDA TM 1 Pentium-III 500MHz (Visual C++ 6.0) experiments / second (according to [ 1 ]) 2 Intel Core i7 2.93GHz (1 core, ANSI C) 7.33 experiments / second 3 NVidia GTX 295 (CUDA C) 890 experiments / second (about 120x speedup) 4 8 GPUs (GTX295+GTX285+Tesla s1070+tesla C2070) 3089 experiments / second (over 400x speedup) The speedup gained allows efficient meta-optimization (parameters tuning) of the algorithms 1 Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
41 SPEEDUP ON CUDA TM 1 Pentium-III 500MHz (Visual C++ 6.0) experiments / second (according to [ 1 ]) 2 Intel Core i7 2.93GHz (1 core, ANSI C) 7.33 experiments / second 3 NVidia GTX 295 (CUDA C) 890 experiments / second (about 120x speedup) 4 8 GPUs (GTX295+GTX285+Tesla s1070+tesla C2070) 3089 experiments / second (over 400x speedup) The speedup gained allows efficient meta-optimization (parameters tuning) of the algorithms 1 Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
42 META-OPTIMIZATION (PARAMETERS TUNING) Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
43 RESULTS OF META-OPTIMIZATION Meta-fitness of the algorithm: knapsack profit at the end of evolution Subject to meta-optimization: rotation angles in quantum genes state space rotation angles θ meta-fitness [ 1 ] 1 Han, K. H., Kim, J. H.: Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary computation, 2000 Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
44 PERFORMANCE COMPARISON Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
45 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
46 PRESENTATION OUTLINE 1 QUANTUM-INSPIRED GENETIC ALGORITHMS 2 NVIDIA CUDA TM TECHNOLOGY 3 TUNING EXPERIMENTAL RESULTS 4 SUMMARY Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
47 SUMMARY In our research: 1 Quantum-Inspired Genetic Algorithm has been implemented in NVidia CUDA TM technology 2 Over 400x speedup has been gained on 8 GPU devices 3 The speedup allows efficient meta-optimization of selected parameters (rotation angles in quantum genes state space) 4 Real-Coded Evolutionary Algorithm has been used as an overlaid meta-optimizer 5 Tuned QIGA algorithm performs much better in the considered combinatorial optimization problem Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
48 MY SELECTED RECENT PAPERS 1 R.Nowotniak, J. Kucharski, Meta-optimization of Quantum-Inspired Evolutionary Algorithm, 2010, Proceedings of the XVII International Conference on Information Technology Systems, ISBN R.Nowotniak, J. Kucharski, Building Blocks Propagation in Quantum-Inspired Genetic Algorithm, 2010, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010, ISSN R. Nowotniak, Survey of Quantum-Inspired Evolutionary Algorithms, 2010, Proceedings of the FIMB PhD students conference, ISSN S.Jeżewski, M. Łaski, R. Nowotniak, Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, 2010, Scientific Bulletin of Academy of Science and Technology, Automatics, ISSN Ł. Jopek, R. Nowotniak, M. Postolski, L. Babout, M. Janaszewski, Application of Quantum Genetic Algorithms in Feature Selection Problem, 2009, Scientific Bulletin of Academy of Science and Technology, Automatics, ISSN Robert Nowotniak, Jacek Kucharski System Modelling and Control, / 19
49 Thank you for your attention Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
50 SELECTED APPLICATIONS 1 Simultaneous Localization and Mapping (SLAM) problem for mobile robots[ 2 ] 2 Segmentation of titanium alloys images obtained with X-Ray microtomography[ 3 ] 2 Jeżewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
51 SELECTED APPLICATIONS 1 Simultaneous Localization and Mapping (SLAM) problem for mobile robots[ 2 ] 2 Segmentation of titanium alloys images obtained with X-Ray microtomography[ 3 ] 2 Jeżewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
52 SELECTED APPLICATIONS 1 Simultaneous Localization and Mapping (SLAM) problem for mobile robots[ 2 ] 2 Segmentation of titanium alloys images obtained with X-Ray microtomography[ 3 ] 2 Jeżewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
53 SELECTED APPLICATIONS 1 Simultaneous Localization and Mapping (SLAM) problem for mobile robots[ 2 ] 2 Segmentation of titanium alloys images obtained with X-Ray microtomography[ 3 ] 2 Jeżewski, S., Łaski, M., Nowotniak, R.: Comparison of Algorithms for Simultaneous Localization and Mapping Problem for Mobile Robot, Scientific Bulletin of Academy of Science and Technology,. Automatics, Jopek, Ł., Nowotniak, R., Postolski, M., Babout, L.., Janaszewski, M.: Application of Quantum Genetic Algorithms to Feature Selection Problem, Scientific Bulletin of Academy of Science and Technology, Automatics, 2010 Robert Nowotniak, Jacek Kucharski System Modelling and Control, 2011
Graphics Cards and Graphics Processing Units. Ben Johnstone Russ Martin November 15, 2011
Graphics Cards and Graphics Processing Units Ben Johnstone Russ Martin November 15, 2011 Contents Graphics Processing Units (GPUs) Graphics Pipeline Architectures 8800-GTX200 Fermi Cayman Performance Analysis
More informationIntroduction to GP-GPUs. Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1
Introduction to GP-GPUs Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1 GPU Architectures: How do we reach here? NVIDIA Fermi, 512 Processing Elements (PEs) 2 What Can It Do?
More informationAlberto Corrales-García, Rafael Rodríguez-Sánchez, José Luis Martínez, Gerardo Fernández-Escribano, José M. Claver and José Luis Sánchez
Alberto Corrales-García, Rafael Rodríguez-Sánchez, José Luis artínez, Gerardo Fernández-Escribano, José. Claver and José Luis Sánchez 1. Introduction 2. Technical Background 3. Proposed DVC to H.264/AVC
More informationLBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR
LBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR Frédéric Kuznik, frederic.kuznik@insa lyon.fr 1 Framework Introduction Hardware architecture CUDA overview Implementation details A simple case:
More informationIntroduction to GPU Computing
Matthis Hauschild Universität Hamburg Fakultät für Mathematik, Informatik und Naturwissenschaften Technische Aspekte Multimodaler Systeme December 4, 2014 M. Hauschild - 1 Table of Contents 1. Architecture
More informationE6895 Advanced Big Data Analytics Lecture 14:! NVIDIA GPU Examples and GPU on ios devices
E6895 Advanced Big Data Analytics Lecture 14: NVIDIA GPU Examples and GPU on ios devices Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist,
More informationGPU System Architecture. Alan Gray EPCC The University of Edinburgh
GPU System Architecture EPCC The University of Edinburgh Outline Why do we want/need accelerators such as GPUs? GPU-CPU comparison Architectural reasons for GPU performance advantages GPU accelerated systems
More informationultra fast SOM using CUDA
ultra fast SOM using CUDA SOM (Self-Organizing Map) is one of the most popular artificial neural network algorithms in the unsupervised learning category. Sijo Mathew Preetha Joy Sibi Rajendra Manoj A
More informationProgramming models for heterogeneous computing. Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga
Programming models for heterogeneous computing Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga Talk outline [30 slides] 1. Introduction [5 slides] 2.
More informationGPU Computing with CUDA Lecture 2 - CUDA Memories. Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile
GPU Computing with CUDA Lecture 2 - CUDA Memories Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile 1 Outline of lecture Recap of Lecture 1 Warp scheduling CUDA Memory hierarchy
More informationParallel Image Processing with CUDA A case study with the Canny Edge Detection Filter
Parallel Image Processing with CUDA A case study with the Canny Edge Detection Filter Daniel Weingaertner Informatics Department Federal University of Paraná - Brazil Hochschule Regensburg 02.05.2011 Daniel
More informationOn the Cost of Mining Very Large Open Source Repositories
On the Cost of Mining Very Large Open Source Repositories Sean Banerjee Carnegie Mellon University Bojan Cukic University of North Carolina at Charlotte BIGDSE, Florence 2015 Introduction Issue tracking
More informationIntroduction to GPU hardware and to CUDA
Introduction to GPU hardware and to CUDA Philip Blakely Laboratory for Scientific Computing, University of Cambridge Philip Blakely (LSC) GPU introduction 1 / 37 Course outline Introduction to GPU hardware
More informationNVIDIA GeForce GTX 580 GPU Datasheet
NVIDIA GeForce GTX 580 GPU Datasheet NVIDIA GeForce GTX 580 GPU Datasheet 3D Graphics Full Microsoft DirectX 11 Shader Model 5.0 support: o NVIDIA PolyMorph Engine with distributed HW tessellation engines
More information~ Greetings from WSU CAPPLab ~
~ Greetings from WSU CAPPLab ~ Multicore with SMT/GPGPU provides the ultimate performance; at WSU CAPPLab, we can help! Dr. Abu Asaduzzaman, Assistant Professor and Director Wichita State University (WSU)
More informationNext Generation GPU Architecture Code-named Fermi
Next Generation GPU Architecture Code-named Fermi The Soul of a Supercomputer in the Body of a GPU Why is NVIDIA at Super Computing? Graphics is a throughput problem paint every pixel within frame time
More informationACCELERATING SELECT WHERE AND SELECT JOIN QUERIES ON A GPU
Computer Science 14 (2) 2013 http://dx.doi.org/10.7494/csci.2013.14.2.243 Marcin Pietroń Pawe l Russek Kazimierz Wiatr ACCELERATING SELECT WHERE AND SELECT JOIN QUERIES ON A GPU Abstract This paper presents
More informationGPUs for Scientific Computing
GPUs for Scientific Computing p. 1/16 GPUs for Scientific Computing Mike Giles mike.giles@maths.ox.ac.uk Oxford-Man Institute of Quantitative Finance Oxford University Mathematical Institute Oxford e-research
More informationGPU Parallel Computing Architecture and CUDA Programming Model
GPU Parallel Computing Architecture and CUDA Programming Model John Nickolls Outline Why GPU Computing? GPU Computing Architecture Multithreading and Arrays Data Parallel Problem Decomposition Parallel
More informationAuto-Tuning TRSM with an Asynchronous Task Assignment Model on Multicore, GPU and Coprocessor Systems
Auto-Tuning TRSM with an Asynchronous Task Assignment Model on Multicore, GPU and Coprocessor Systems Murilo Boratto Núcleo de Arquitetura de Computadores e Sistemas Operacionais, Universidade do Estado
More informationLecture 3: Modern GPUs A Hardware Perspective Mohamed Zahran (aka Z) mzahran@cs.nyu.edu http://www.mzahran.com
CSCI-GA.3033-012 Graphics Processing Units (GPUs): Architecture and Programming Lecture 3: Modern GPUs A Hardware Perspective Mohamed Zahran (aka Z) mzahran@cs.nyu.edu http://www.mzahran.com Modern GPU
More informationReal-time Visual Tracker by Stream Processing
Real-time Visual Tracker by Stream Processing Simultaneous and Fast 3D Tracking of Multiple Faces in Video Sequences by Using a Particle Filter Oscar Mateo Lozano & Kuzahiro Otsuka presented by Piotr Rudol
More informationGraphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:
More informationGraphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:
More informationGPU Renderfarm with Integrated Asset Management & Production System (AMPS)
GPU Renderfarm with Integrated Asset Management & Production System (AMPS) Tackling two main challenges in CG movie production Presenter: Dr. Chen Quan Multi-plAtform Game Innovation Centre (MAGIC), Nanyang
More informationGPU File System Encryption Kartik Kulkarni and Eugene Linkov
GPU File System Encryption Kartik Kulkarni and Eugene Linkov 5/10/2012 SUMMARY. We implemented a file system that encrypts and decrypts files. The implementation uses the AES algorithm computed through
More informationPurchase of High Performance Computing (HPC) Central Compute Resources by Northwestern Researchers
Information Technology Purchase of High Performance Computing (HPC) Central Compute Resources by Northwestern Researchers Effective for FY2016 Purpose This document summarizes High Performance Computing
More informationFast Implementations of AES on Various Platforms
Fast Implementations of AES on Various Platforms Joppe W. Bos 1 Dag Arne Osvik 1 Deian Stefan 2 1 EPFL IC IIF LACAL, Station 14, CH-1015 Lausanne, Switzerland {joppe.bos, dagarne.osvik}@epfl.ch 2 Dept.
More informationOpenCL Optimization. San Jose 10/2/2009 Peng Wang, NVIDIA
OpenCL Optimization San Jose 10/2/2009 Peng Wang, NVIDIA Outline Overview The CUDA architecture Memory optimization Execution configuration optimization Instruction optimization Summary Overall Optimization
More informationANALYSIS OF RSA ALGORITHM USING GPU PROGRAMMING
ANALYSIS OF RSA ALGORITHM USING GPU PROGRAMMING Sonam Mahajan 1 and Maninder Singh 2 1 Department of Computer Science Engineering, Thapar University, Patiala, India 2 Department of Computer Science Engineering,
More informationL20: GPU Architecture and Models
L20: GPU Architecture and Models scribe(s): Abdul Khalifa 20.1 Overview GPUs (Graphics Processing Units) are large parallel structure of processing cores capable of rendering graphics efficiently on displays.
More informationNVIDIA CUDA Software and GPU Parallel Computing Architecture. David B. Kirk, Chief Scientist
NVIDIA CUDA Software and GPU Parallel Computing Architecture David B. Kirk, Chief Scientist Outline Applications of GPU Computing CUDA Programming Model Overview Programming in CUDA The Basics How to Get
More informationGeoImaging Accelerator Pansharp Test Results
GeoImaging Accelerator Pansharp Test Results Executive Summary After demonstrating the exceptional performance improvement in the orthorectification module (approximately fourteen-fold see GXL Ortho Performance
More informationSpeeding Up RSA Encryption Using GPU Parallelization
2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation Speeding Up RSA Encryption Using GPU Parallelization Chu-Hsing Lin, Jung-Chun Liu, and Cheng-Chieh Li Department of
More informationSCATTERED DATA VISUALIZATION USING GPU. A Thesis. Presented to. The Graduate Faculty of The University of Akron. In Partial Fulfillment
SCATTERED DATA VISUALIZATION USING GPU A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Bo Cai May, 2015
More informationIntroduction to Numerical General Purpose GPU Computing with NVIDIA CUDA. Part 1: Hardware design and programming model
Introduction to Numerical General Purpose GPU Computing with NVIDIA CUDA Part 1: Hardware design and programming model Amin Safi Faculty of Mathematics, TU dortmund January 22, 2016 Table of Contents Set
More informationClustering Billions of Data Points Using GPUs
Clustering Billions of Data Points Using GPUs Ren Wu ren.wu@hp.com Bin Zhang bin.zhang2@hp.com Meichun Hsu meichun.hsu@hp.com ABSTRACT In this paper, we report our research on using GPUs to accelerate
More informationFast Software AES Encryption
Calhoun: The NPS Institutional Archive Faculty and Researcher Publications Faculty and Researcher Publications 2010 Fast Software AES Encryption Osvik, Dag Arne Proceedings FSE'10 Proceedings of the 17th
More informationIntroduction to GPU Programming Languages
CSC 391/691: GPU Programming Fall 2011 Introduction to GPU Programming Languages Copyright 2011 Samuel S. Cho http://www.umiacs.umd.edu/ research/gpu/facilities.html Maryland CPU/GPU Cluster Infrastructure
More informationStream Processing on GPUs Using Distributed Multimedia Middleware
Stream Processing on GPUs Using Distributed Multimedia Middleware Michael Repplinger 1,2, and Philipp Slusallek 1,2 1 Computer Graphics Lab, Saarland University, Saarbrücken, Germany 2 German Research
More informationAccelerating variant calling
Accelerating variant calling Mauricio Carneiro GSA Broad Institute Intel Genomic Sequencing Pipeline Workshop Mount Sinai 12/10/2013 This is the work of many Genome sequencing and analysis team Mark DePristo
More informationBenchmark Hadoop and Mars: MapReduce on cluster versus on GPU
Benchmark Hadoop and Mars: MapReduce on cluster versus on GPU Heshan Li, Shaopeng Wang The Johns Hopkins University 3400 N. Charles Street Baltimore, Maryland 21218 {heshanli, shaopeng}@cs.jhu.edu 1 Overview
More informationParallel Prefix Sum (Scan) with CUDA. Mark Harris mharris@nvidia.com
Parallel Prefix Sum (Scan) with CUDA Mark Harris mharris@nvidia.com April 2007 Document Change History Version Date Responsible Reason for Change February 14, 2007 Mark Harris Initial release April 2007
More informationISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013
Transistor Level Fault Finding in VLSI Circuits using Genetic Algorithm Lalit A. Patel, Sarman K. Hadia CSPIT, CHARUSAT, Changa., CSPIT, CHARUSAT, Changa Abstract This paper presents, genetic based algorithm
More informationOptimizing a 3D-FWT code in a cluster of CPUs+GPUs
Optimizing a 3D-FWT code in a cluster of CPUs+GPUs Gregorio Bernabé Javier Cuenca Domingo Giménez Universidad de Murcia Scientific Computing and Parallel Programming Group XXIX Simposium Nacional de la
More informationGPU Performance Analysis and Optimisation
GPU Performance Analysis and Optimisation Thomas Bradley, NVIDIA Corporation Outline What limits performance? Analysing performance: GPU profiling Exposing sufficient parallelism Optimising for Kepler
More informationAccelerating CFD using OpenFOAM with GPUs
Accelerating CFD using OpenFOAM with GPUs Authors: Saeed Iqbal and Kevin Tubbs The OpenFOAM CFD Toolbox is a free, open source CFD software package produced by OpenCFD Ltd. Its user base represents a wide
More informationOpenPOWER Outlook AXEL KOEHLER SR. SOLUTION ARCHITECT HPC
OpenPOWER Outlook AXEL KOEHLER SR. SOLUTION ARCHITECT HPC Driving industry innovation The goal of the OpenPOWER Foundation is to create an open ecosystem, using the POWER Architecture to share expertise,
More informationGPU for Scientific Computing. -Ali Saleh
1 GPU for Scientific Computing -Ali Saleh Contents Introduction What is GPU GPU for Scientific Computing K-Means Clustering K-nearest Neighbours When to use GPU and when not Commercial Programming GPU
More informationHigh Performance CUDA Accelerated Local Optimization in Traveling Salesman Problem
High Performance CUDA Accelerated Local Optimization in Traveling Salesman Problem Kamil Rocki, PhD Department of Computer Science Graduate School of Information Science and Technology The University of
More informationCUDA programming on NVIDIA GPUs
p. 1/21 on NVIDIA GPUs Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute Oxford-Man Institute for Quantitative Finance Oxford eresearch Centre p. 2/21 Overview hardware view
More informationGPU-based Decompression for Medical Imaging Applications
GPU-based Decompression for Medical Imaging Applications Al Wegener, CTO Samplify Systems 160 Saratoga Ave. Suite 150 Santa Clara, CA 95051 sales@samplify.com (888) LESS-BITS +1 (408) 249-1500 1 Outline
More informationTurbomachinery CFD on many-core platforms experiences and strategies
Turbomachinery CFD on many-core platforms experiences and strategies Graham Pullan Whittle Laboratory, Department of Engineering, University of Cambridge MUSAF Colloquium, CERFACS, Toulouse September 27-29
More informationCFD Implementation with In-Socket FPGA Accelerators
CFD Implementation with In-Socket FPGA Accelerators Ivan Gonzalez UAM Team at DOVRES FuSim-E Programme Symposium: CFD on Future Architectures C 2 A 2 S 2 E DLR Braunschweig 14 th -15 th October 2009 Outline
More informationGuided Performance Analysis with the NVIDIA Visual Profiler
Guided Performance Analysis with the NVIDIA Visual Profiler Identifying Performance Opportunities NVIDIA Nsight Eclipse Edition (nsight) NVIDIA Visual Profiler (nvvp) nvprof command-line profiler Guided
More informationApplications to Computational Financial and GPU Computing. May 16th. Dr. Daniel Egloff +41 44 520 01 17 +41 79 430 03 61
F# Applications to Computational Financial and GPU Computing May 16th Dr. Daniel Egloff +41 44 520 01 17 +41 79 430 03 61 Today! Why care about F#? Just another fashion?! Three success stories! How Alea.cuBase
More informationCell-SWat: Modeling and Scheduling Wavefront Computations on the Cell Broadband Engine
Cell-SWat: Modeling and Scheduling Wavefront Computations on the Cell Broadband Engine Ashwin Aji, Wu Feng, Filip Blagojevic and Dimitris Nikolopoulos Forecast Efficient mapping of wavefront algorithms
More informationAccelerating BIRCH for Clustering Large Scale Streaming Data Using CUDA Dynamic Parallelism
Accelerating BIRCH for Clustering Large Scale Streaming Data Using CUDA Dynamic Parallelism Jianqiang Dong, Fei Wang and Bo Yuan Intelligent Computing Lab, Division of Informatics Graduate School at Shenzhen,
More informationHPC with Multicore and GPUs
HPC with Multicore and GPUs Stan Tomov Electrical Engineering and Computer Science Department University of Tennessee, Knoxville CS 594 Lecture Notes March 4, 2015 1/18 Outline! Introduction - Hardware
More informationString Matching on a multicore GPU using CUDA
String Matching on a multicore GPU using CUDA Charalampos S. Kouzinopoulos and Konstantinos G. Margaritis Parallel and Distributed Processing Laboratory Department of Applied Informatics, University of
More informationGPGPU Computing. Yong Cao
GPGPU Computing Yong Cao Why Graphics Card? It s powerful! A quiet trend Copyright 2009 by Yong Cao Why Graphics Card? It s powerful! Processor Processing Units FLOPs per Unit Clock Speed Processing Power
More informationRetargeting PLAPACK to Clusters with Hardware Accelerators
Retargeting PLAPACK to Clusters with Hardware Accelerators Manuel Fogué 1 Francisco Igual 1 Enrique S. Quintana-Ortí 1 Robert van de Geijn 2 1 Departamento de Ingeniería y Ciencia de los Computadores.
More informationTrends in High-Performance Computing for Power Grid Applications
Trends in High-Performance Computing for Power Grid Applications Franz Franchetti ECE, Carnegie Mellon University www.spiral.net Co-Founder, SpiralGen www.spiralgen.com This talk presents my personal views
More informationQCD as a Video Game?
QCD as a Video Game? Sándor D. Katz Eötvös University Budapest in collaboration with Győző Egri, Zoltán Fodor, Christian Hoelbling Dániel Nógrádi, Kálmán Szabó Outline 1. Introduction 2. GPU architecture
More informationGTC 2014 San Jose, California
GTC 2014 San Jose, California An Approach to Parallel Processing of Big Data in Finance for Alpha Generation and Risk Management Yigal Jhirad and Blay Tarnoff March 26, 2014 GTC 2014: Table of Contents
More informationDCT-JPEG Image Coding Based on GPU
, pp. 293-302 http://dx.doi.org/10.14257/ijhit.2015.8.5.32 DCT-JPEG Image Coding Based on GPU Rongyang Shan 1, Chengyou Wang 1*, Wei Huang 2 and Xiao Zhou 1 1 School of Mechanical, Electrical and Information
More informationHETEROGENEOUS HPC, ARCHITECTURE OPTIMIZATION, AND NVLINK
HETEROGENEOUS HPC, ARCHITECTURE OPTIMIZATION, AND NVLINK Steve Oberlin CTO, Accelerated Computing US to Build Two Flagship Supercomputers SUMMIT SIERRA Partnership for Science 100-300 PFLOPS Peak Performance
More informationHome Exam 3: Distributed Video Encoding using Dolphin PCI Express Networks. October 20 th 2015
INF5063: Programming heterogeneous multi-core processors because the OS-course is just to easy! Home Exam 3: Distributed Video Encoding using Dolphin PCI Express Networks October 20 th 2015 Håkon Kvale
More information1. INTRODUCTION Graphics 2
1. INTRODUCTION Graphics 2 06-02408 Level 3 10 credits in Semester 2 Professor Aleš Leonardis Slides by Professor Ela Claridge What is computer graphics? The art of 3D graphics is the art of fooling the
More informationStock Trading Strategy Creation Using GP on GPU
Stock Trading Strategy Creation Using GP on GPU Dave McKenney School of Computer Science Carleton University Ottawa, Canada K1S 5B6 dmckenne@connect.carleton.ca December 19, 2010 Abstract This paper investigates
More informationParallel Simplification of Large Meshes on PC Clusters
Parallel Simplification of Large Meshes on PC Clusters Hua Xiong, Xiaohong Jiang, Yaping Zhang, Jiaoying Shi State Key Lab of CAD&CG, College of Computer Science Zhejiang University Hangzhou, China April
More informationMemory Allocation Technique for Segregated Free List Based on Genetic Algorithm
Journal of Al-Nahrain University Vol.15 (2), June, 2012, pp.161-168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College
More informationParallel Compression and Decompression of DNA Sequence Reads in FASTQ Format
, pp.91-100 http://dx.doi.org/10.14257/ijhit.2014.7.4.09 Parallel Compression and Decompression of DNA Sequence Reads in FASTQ Format Jingjing Zheng 1,* and Ting Wang 1, 2 1,* Parallel Software and Computational
More informationThe Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System
The Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System Qingyu Meng, Alan Humphrey, Martin Berzins Thanks to: John Schmidt and J. Davison de St. Germain, SCI Institute Justin Luitjens
More informationNVIDIA CUDA GETTING STARTED GUIDE FOR MAC OS X
NVIDIA CUDA GETTING STARTED GUIDE FOR MAC OS X DU-05348-001_v5.5 July 2013 Installation and Verification on Mac OS X TABLE OF CONTENTS Chapter 1. Introduction...1 1.1. System Requirements... 1 1.2. About
More informationControl 2004, University of Bath, UK, September 2004
Control, University of Bath, UK, September ID- IMPACT OF DEPENDENCY AND LOAD BALANCING IN MULTITHREADING REAL-TIME CONTROL ALGORITHMS M A Hossain and M O Tokhi Department of Computing, The University of
More informationParallel Computing with MATLAB
Parallel Computing with MATLAB Scott Benway Senior Account Manager Jiro Doke, Ph.D. Senior Application Engineer 2013 The MathWorks, Inc. 1 Acceleration Strategies Applied in MATLAB Approach Options Best
More informationTexture Cache Approximation on GPUs
Texture Cache Approximation on GPUs Mark Sutherland Joshua San Miguel Natalie Enright Jerger {suther68,enright}@ece.utoronto.ca, joshua.sanmiguel@mail.utoronto.ca 1 Our Contribution GPU Core Cache Cache
More informationSpeeding Up Evolutionary Learning Algorithms using GPUs
Speeding Up Evolutionary Learning Algorithms using GPUs Alberto Cano Amelia Zafra Sebastián Ventura Department of Computing and Numerical Analysis, University of Córdoba, 1471 Córdoba, Spain {i52caroa,azafra,sventura}@uco.es
More informationA Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number
A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number 1 Tomohiro KAMIMURA, 2 Akinori KANASUGI 1 Department of Electronics, Tokyo Denki University, 07ee055@ms.dendai.ac.jp
More informationPricing of cross-currency interest rate derivatives on Graphics Processing Units
Pricing of cross-currency interest rate derivatives on Graphics Processing Units Duy Minh Dang Department of Computer Science University of Toronto Toronto, Canada dmdang@cs.toronto.edu Joint work with
More informationLecture 11: Multi-Core and GPU. Multithreading. Integration of multiple processor cores on a single chip.
Lecture 11: Multi-Core and GPU Multi-core computers Multithreading GPUs General Purpose GPUs Zebo Peng, IDA, LiTH 1 Multi-Core System Integration of multiple processor cores on a single chip. To provide
More informationOptimizing Parallel Reduction in CUDA. Mark Harris NVIDIA Developer Technology
Optimizing Parallel Reduction in CUDA Mark Harris NVIDIA Developer Technology Parallel Reduction Common and important data parallel primitive Easy to implement in CUDA Harder to get it right Serves as
More informationTowards Large-Scale Molecular Dynamics Simulations on Graphics Processors
Towards Large-Scale Molecular Dynamics Simulations on Graphics Processors Joe Davis, Sandeep Patel, and Michela Taufer University of Delaware Outline Introduction Introduction to GPU programming Why MD
More informationIP Video Rendering Basics
CohuHD offers a broad line of High Definition network based cameras, positioning systems and VMS solutions designed for the performance requirements associated with critical infrastructure applications.
More informationGPU Accelerated Monte Carlo Simulations and Time Series Analysis
GPU Accelerated Monte Carlo Simulations and Time Series Analysis Institute of Physics, Johannes Gutenberg-University of Mainz Center for Polymer Studies, Department of Physics, Boston University Artemis
More informationBinary search tree with SIMD bandwidth optimization using SSE
Binary search tree with SIMD bandwidth optimization using SSE Bowen Zhang, Xinwei Li 1.ABSTRACT In-memory tree structured index search is a fundamental database operation. Modern processors provide tremendous
More informationExperiences on using GPU accelerators for data analysis in ROOT/RooFit
Experiences on using GPU accelerators for data analysis in ROOT/RooFit Sverre Jarp, Alfio Lazzaro, Julien Leduc, Yngve Sneen Lindal, Andrzej Nowak European Organization for Nuclear Research (CERN), Geneva,
More informationHow to program efficient optimization algorithms on Graphics Processing Units - The Vehicle Routing Problem as a case study
How to program efficient optimization algorithms on Graphics Processing Units - The Vehicle Routing Problem as a case study Geir Hasle, Christian Schulz Department of, SINTEF ICT, Oslo, Norway Seminar
More informationParallel Programming Survey
Christian Terboven 02.09.2014 / Aachen, Germany Stand: 26.08.2014 Version 2.3 IT Center der RWTH Aachen University Agenda Overview: Processor Microarchitecture Shared-Memory
More informationPerformance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries
Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries Shin Morishima 1 and Hiroki Matsutani 1,2,3 1Keio University, 3 14 1 Hiyoshi, Kohoku ku, Yokohama, Japan 2National Institute
More informationNVIDIA VIDEO ENCODER 5.0
NVIDIA VIDEO ENCODER 5.0 NVENC_DA-06209-001_v06 November 2014 Application Note NVENC - NVIDIA Hardware Video Encoder 5.0 NVENC_DA-06209-001_v06 i DOCUMENT CHANGE HISTORY NVENC_DA-06209-001_v06 Version
More informationCUDA Basics. Murphy Stein New York University
CUDA Basics Murphy Stein New York University Overview Device Architecture CUDA Programming Model Matrix Transpose in CUDA Further Reading What is CUDA? CUDA stands for: Compute Unified Device Architecture
More informationCase Study on Productivity and Performance of GPGPUs
Case Study on Productivity and Performance of GPGPUs Sandra Wienke wienke@rz.rwth-aachen.de ZKI Arbeitskreis Supercomputing April 2012 Rechen- und Kommunikationszentrum (RZ) RWTH GPU-Cluster 56 Nvidia
More informationPerformance evaluation of CUDA programming for 5-axis machining multi-scale simulation
Performance evaluation of CUDA programming for 5-axis machining multi-scale simulation Felix Abecassis, Sylvain Lavernhe, Christophe Tournier, Pierre-Alain Boucard To cite this version: Felix Abecassis,
More informationGPGPU accelerated Computational Fluid Dynamics
t e c h n i s c h e u n i v e r s i t ä t b r a u n s c h w e i g Carl-Friedrich Gauß Faculty GPGPU accelerated Computational Fluid Dynamics 5th GACM Colloquium on Computational Mechanics Hamburg Institute
More informationA GPU COMPUTING PLATFORM (SAGA) AND A CFD CODE ON GPU FOR AEROSPACE APPLICATIONS
A GPU COMPUTING PLATFORM (SAGA) AND A CFD CODE ON GPU FOR AEROSPACE APPLICATIONS SUDHAKARAN.G APCF, AERO, VSSC, ISRO 914712564742 g_suhakaran@vssc.gov.in THOMAS.C.BABU APCF, AERO, VSSC, ISRO 914712565833
More informationCLOUD GAMING WITH NVIDIA GRID TECHNOLOGIES Franck DIARD, Ph.D., SW Chief Software Architect GDC 2014
CLOUD GAMING WITH NVIDIA GRID TECHNOLOGIES Franck DIARD, Ph.D., SW Chief Software Architect GDC 2014 Introduction Cloud ification < 2013 2014+ Music, Movies, Books Games GPU Flops GPUs vs. Consoles 10,000
More informationOverview. Lecture 1: an introduction to CUDA. Hardware view. Hardware view. hardware view software view CUDA programming
Overview Lecture 1: an introduction to CUDA Mike Giles mike.giles@maths.ox.ac.uk hardware view software view Oxford University Mathematical Institute Oxford e-research Centre Lecture 1 p. 1 Lecture 1 p.
More informationMedical Image Processing on the GPU. Past, Present and Future. Anders Eklund, PhD Virginia Tech Carilion Research Institute andek@vtc.vt.
Medical Image Processing on the GPU Past, Present and Future Anders Eklund, PhD Virginia Tech Carilion Research Institute andek@vtc.vt.edu Outline Motivation why do we need GPUs? Past - how was GPU programming
More information