Das Ising-Modell auf Grafikkarten

Size: px
Start display at page:

Download "Das Ising-Modell auf Grafikkarten"

Transcription

1 Das Ising-Modell auf Grafikkarten Institute of Physics, Johannes Gutenberg-University of Mainz Center for Polymer Studies, Department of Physics, Boston University Artemis Capital Asset Management GmbH With thanks to: Benjamin Block, Peter Virnau, Wolfgang Paul, Johannes J. Schneider

2 GPGPU computing GT200 single precision G80 Realistic illustrations Driving force: computer game industry NV GHz Harpertown Source: NVIDIA CUDA programming guide

3 GPU device architecture GeForce GTX 280: Global memory 1024 MB Number of multiprocessors 30 Number of cores 240 Constant memory 64 kb Shared memory 16 kb Clock rate 1.30 GHz

4 GPU device / Reference system GeForce GTX 280: Global memory 1024 MB Number of multiprocessors 30 Number of cores 240 Constant memory 64 kb Reference CPU: Intel Core 2 Quad 2.66 GHz Shared memory 16 kb Clock rate 1.30 GHz Cache size 4096 KB

5 C code with extensions global void gpu_function(int n, float* a, float* b) { } //Determine array element int i = threadidx.x + blockidx.x * blockdim.x; if(i<n) b[i] += a[i] * a[i]; Block 0 Block 1 Block 2... host void cpu_function() { int n = 128 * 128; int n_blocks = 128; int n_threads = 128; gpu_function<<<n_blocks,n_threads>>>(n, a, b); // Global barrier between GPU functions Thread 0 Block 1 Thread 1 Thread 2... } gpu_function<<<n_blocks/2,n_threads*2>>>(n, a, b);

6 Linear congruential RNGs x i+1,j =(a x i,j + c) mod m x 0,j+1 = (16807 x 0,j ) mod m a = c = bit architecture provided by the GPU x i,j [ 2 31 ;2 31 1] y i,j = abs ( x i,j /2 31) abs (x i,j )

7 Computation times Random numbers Time [ms] Acceleration " s Time on GPU for allocation Time on GPU for memory transfer Time on GPU for main function Total processing time on GPU Total processing time on CPU Speedup factor β = Total processing time on CPU Total processing time on GPU 10!1 10! Block number s

8 Ising model H = J i,j S i S j H i S i nearest neighbors Spin update: Metropolis criterion W a b = exp( H /k B T ) if H > 0 W a b =1 H 0 if

9 2D Ising: GPU implementation Noninteracting domains where Monte Carlo moves are performed in parallel Checkerboard algoritm

10 Computation times 2D Time [ms] Acceleration " n/2 Time on GPU for allocation Time on GPU for memory transfer Time on GPU for main function Total processing time on GPU Total processing time on CPU Speedup factor ! Block size n/2

11 Binder cumulant 2D kbtc = J "M (T )4 # U4 (T ) = 1 3"M (T )2 # U4 0.5 Critical temperature n/2 = 16 n/2 = 32 n/2 = 64 n/2 = 128 n/2 = kbt [J]

12 Ising model 3D H = J i,j S i S j H i S i nearest neighbors

13 3D Ising: GPU implementation Noninteracting domains where Monte Carlo moves are performed in parallel

14 Computation times 3D Time [ms] Acceleration " n/2 Time on GPU for allocation Time on GPU for memory transfer Time on GPU for main function Total processing time on GPU Total processing time on CPU Speedup factor ! Block size n/2

15 T. Preis, P. Virnau, W. Paul, and J. J. Schneider, J. Comp. Phys. 228, (2009) Binder cumulant 3D k B T C = { J, J} Critical temperature U U 4 (T )=1 M(T )4 3 M(T ) n/2 = 16 n/2 = 32 n/2 = 64 n/2 = k B T [J]

16 B. Block, P. Virnau, and T. Preis, manuscript in preparation Multi-GPU version 2D Communicated by MPI

17 B. Block, P. Virnau, and T. Preis, manuscript in preparation Multi-GPU Computation times Time [ms] Acceleration! n Time on 1 CPU Time on single GPU Time on 4 GPUs Time on 2 GPUs in 2 machines Speedup: n

18 Final remarks, Peter Virnau, Wolfgang Paul, and Johannes J. Schneider, GPU Accelerated Monte Carlo Simulation of 2D and 3D Ising Model, J. Comp. Phys. 228, (2009), Peter Virnau, Wolfgang Paul, and Johannes J. Schneider, Accelerated fluctuation analysis by graphic cards and complex pattern formation in financial markets, New J. Phys. 11, (2009) Source code available:

19 Thank you!

GPU Accelerated Monte Carlo Simulations and Time Series Analysis

GPU 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 information

Overview. Lecture 1: an introduction to CUDA. Hardware view. Hardware view. hardware view software view CUDA programming

Overview. 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 information

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 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 information

Introduction to GPU hardware and to CUDA

Introduction 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 information

Introduction 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 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 information

GPU Hardware and Programming Models. Jeremy Appleyard, September 2015

GPU Hardware and Programming Models. Jeremy Appleyard, September 2015 GPU Hardware and Programming Models Jeremy Appleyard, September 2015 A brief history of GPUs In this talk Hardware Overview Programming Models Ask questions at any point! 2 A Brief History of GPUs 3 Once

More information

GPUs for Scientific Computing

GPUs 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 information

LBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR

LBM 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 information

Lecture 1: an introduction to CUDA

Lecture 1: an introduction to CUDA Lecture 1: an introduction to CUDA Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute Oxford e-research Centre Lecture 1 p. 1 Overview hardware view software view CUDA programming

More information

CUDA programming on NVIDIA GPUs

CUDA 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 information

Introduction 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 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 information

Image Processing & Video Algorithms with CUDA

Image Processing & Video Algorithms with CUDA Image Processing & Video Algorithms with CUDA Eric Young & Frank Jargstorff 8 NVIDIA Corporation. introduction Image processing is a natural fit for data parallel processing Pixels can be mapped directly

More information

CUDA Basics. Murphy Stein New York University

CUDA 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 information

Introduction to GPU Programming Languages

Introduction 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 information

GPGPU Computing. Yong Cao

GPGPU 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 information

Introduction GPU Hardware GPU Computing Today GPU Computing Example Outlook Summary. GPU Computing. Numerical Simulation - from Models to Software

Introduction GPU Hardware GPU Computing Today GPU Computing Example Outlook Summary. GPU Computing. Numerical Simulation - from Models to Software GPU Computing Numerical Simulation - from Models to Software Andreas Barthels JASS 2009, Course 2, St. Petersburg, Russia Prof. Dr. Sergey Y. Slavyanov St. Petersburg State University Prof. Dr. Thomas

More information

The Uintah Framework: A Unified Heterogeneous Task Scheduling and Runtime System

The 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 information

GPU Parallel Computing Architecture and CUDA Programming Model

GPU 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 information

Monte-Carlo Option Pricing. Victor Podlozhnyuk vpodlozhnyuk@nvidia.com

Monte-Carlo Option Pricing. Victor Podlozhnyuk vpodlozhnyuk@nvidia.com Monte-Carlo Option Pricing Victor Podlozhnyuk vpodlozhnyuk@nvidia.com Document Change History Version Date Responsible Reason for Change 1. 3//7 vpodlozhnyuk Initial release Abstract The pricing of options

More information

ultra fast SOM using CUDA

ultra 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 information

The Evolution of Computer Graphics. SVP, Content & Technology, NVIDIA

The Evolution of Computer Graphics. SVP, Content & Technology, NVIDIA The Evolution of Computer Graphics Tony Tamasi SVP, Content & Technology, NVIDIA Graphics Make great images intricate shapes complex optical effects seamless motion Make them fast invent clever techniques

More information

Parallel Prefix Sum (Scan) with CUDA. Mark Harris mharris@nvidia.com

Parallel 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 information

Parallel 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 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 information

Towards Large-Scale Molecular Dynamics Simulations on Graphics Processors

Towards 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 information

GPU Computing with CUDA Lecture 4 - Optimizations. Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile

GPU Computing with CUDA Lecture 4 - Optimizations. Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile GPU Computing with CUDA Lecture 4 - Optimizations Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile 1 Outline of lecture Recap of Lecture 3 Control flow Coalescing Latency hiding

More information

Texture Cache Approximation on GPUs

Texture 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 information

HPC with Multicore and GPUs

HPC 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 information

Introduction to GPU Computing

Introduction 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 information

Introduction to GPGPU. Tiziano Diamanti t.diamanti@cineca.it

Introduction to GPGPU. Tiziano Diamanti t.diamanti@cineca.it t.diamanti@cineca.it Agenda From GPUs to GPGPUs GPGPU architecture CUDA programming model Perspective projection Vectors that connect the vanishing point to every point of the 3D model will intersecate

More information

Lecture 11: Multi-Core and GPU. Multithreading. Integration of multiple processor cores on a single chip.

Lecture 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 information

NVIDIA 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 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 information

GPU System Architecture. Alan Gray EPCC The University of Edinburgh

GPU 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 information

Real-time Visual Tracker by Stream Processing

Real-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 information

Clustering Billions of Data Points Using GPUs

Clustering 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 information

OpenCL Optimization. San Jose 10/2/2009 Peng Wang, NVIDIA

OpenCL 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 information

Pedraforca: ARM + GPU prototype

Pedraforca: ARM + GPU prototype www.bsc.es Pedraforca: ARM + GPU prototype Filippo Mantovani Workshop on exascale and PRACE prototypes Barcelona, 20 May 2014 Overview Goals: Test the performance, scalability, and energy efficiency of

More information

GPU Computing with CUDA Lecture 3 - Efficient Shared Memory Use. Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile

GPU Computing with CUDA Lecture 3 - Efficient Shared Memory Use. Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile GPU Computing with CUDA Lecture 3 - Efficient Shared Memory Use Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile 1 Outline of lecture Recap of Lecture 2 Shared memory in detail

More information

15-418 Final Project Report. Trading Platform Server

15-418 Final Project Report. Trading Platform Server 15-418 Final Project Report Yinghao Wang yinghaow@andrew.cmu.edu May 8, 214 Trading Platform Server Executive Summary The final project will implement a trading platform server that provides back-end support

More information

Experiences on using GPU accelerators for data analysis in ROOT/RooFit

Experiences 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 information

GPU 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 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 information

SCATTERED 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 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 information

Speeding Up RSA Encryption Using GPU Parallelization

Speeding 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 information

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi I.J. Information Technology and Computer Science, 2015, 09, 8-14 Published Online August 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2015.09.02 Modern Platform for Parallel Algorithms

More information

GPU File System Encryption Kartik Kulkarni and Eugene Linkov

GPU 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 information

Introduction to GPU Architecture

Introduction to GPU Architecture Introduction to GPU Architecture Ofer Rosenberg, PMTS SW, OpenCL Dev. Team AMD Based on From Shader Code to a Teraflop: How GPU Shader Cores Work, By Kayvon Fatahalian, Stanford University Content 1. Three

More information

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software SUBMITTED TO IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS 1 Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software K.A. Hawick, Member, IEEE, A. Leist, and D.P. Playne Abstract Recent technological

More information

Parallel Computing with MATLAB

Parallel 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 information

GPU Architecture. Michael Doggett ATI

GPU Architecture. Michael Doggett ATI GPU Architecture Michael Doggett ATI GPU Architecture RADEON X1800/X1900 Microsoft s XBOX360 Xenos GPU GPU research areas ATI - Driving the Visual Experience Everywhere Products from cell phones to super

More information

CUDA Debugging. GPGPU Workshop, August 2012. Sandra Wienke Center for Computing and Communication, RWTH Aachen University

CUDA Debugging. GPGPU Workshop, August 2012. Sandra Wienke Center for Computing and Communication, RWTH Aachen University CUDA Debugging GPGPU Workshop, August 2012 Sandra Wienke Center for Computing and Communication, RWTH Aachen University Nikolay Piskun, Chris Gottbrath Rogue Wave Software Rechen- und Kommunikationszentrum

More information

Stochastic Analysis of a Queue Length Model Using a Graphics Processing Unit

Stochastic Analysis of a Queue Length Model Using a Graphics Processing Unit Stochastic Analysis of a ueue Length Model Using a Graphics Processing Unit J. Přiryl* Faculty of Transportation Sciences, Czech University of Technology, Prague, Czech Republic Institute of Information

More information

Hardware-Aware Analysis and. Presentation Date: Sep 15 th 2009 Chrissie C. Cui

Hardware-Aware Analysis and. Presentation Date: Sep 15 th 2009 Chrissie C. Cui Hardware-Aware Analysis and Optimization of Stable Fluids Presentation Date: Sep 15 th 2009 Chrissie C. Cui Outline Introduction Highlights Flop and Bandwidth Analysis Mehrstellen Schemes Advection Caching

More information

L20: GPU Architecture and Models

L20: 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 information

Learn CUDA in an Afternoon: Hands-on Practical Exercises

Learn CUDA in an Afternoon: Hands-on Practical Exercises Learn CUDA in an Afternoon: Hands-on Practical Exercises Alan Gray and James Perry, EPCC, The University of Edinburgh Introduction This document forms the hands-on practical component of the Learn CUDA

More information

GPU Computing. The GPU Advantage. To ExaScale and Beyond. The GPU is the Computer

GPU Computing. The GPU Advantage. To ExaScale and Beyond. The GPU is the Computer GU Computing 1 2 3 The GU Advantage To ExaScale and Beyond The GU is the Computer The GU Advantage The GU Advantage A Tale of Two Machines Tianhe-1A at NSC Tianjin Tianhe-1A at NSC Tianjin The World s

More information

Experiences With Mobile Processors for Energy Efficient HPC

Experiences With Mobile Processors for Energy Efficient HPC Experiences With Mobile Processors for Energy Efficient HPC Nikola Rajovic, Alejandro Rico, James Vipond, Isaac Gelado, Nikola Puzovic, Alex Ramirez Barcelona Supercomputing Center Universitat Politècnica

More information

GPU Performance Analysis and Optimisation

GPU 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 information

Next Generation GPU Architecture Code-named Fermi

Next 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 information

~ Greetings from WSU CAPPLab ~

~ 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 information

NVIDIA GeForce GTX 580 GPU Datasheet

NVIDIA 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

Acceleration of a CFD Code with a GPU

Acceleration of a CFD Code with a GPU Acceleration of a CFD Code with a GPU Dennis C. Jespersen ABSTRACT The Computational Fluid Dynamics code Overflow includes as one of its solver options an algorithm which is a fairly small piece of code

More information

A 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 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 information

CUDA Programming. Week 4. Shared memory and register

CUDA Programming. Week 4. Shared memory and register CUDA Programming Week 4. Shared memory and register Outline Shared memory and bank confliction Memory padding Register allocation Example of matrix-matrix multiplication Homework SHARED MEMORY AND BANK

More information

MONTE-CARLO SIMULATION OF AMERICAN OPTIONS WITH GPUS. Julien Demouth, NVIDIA

MONTE-CARLO SIMULATION OF AMERICAN OPTIONS WITH GPUS. Julien Demouth, NVIDIA MONTE-CARLO SIMULATION OF AMERICAN OPTIONS WITH GPUS Julien Demouth, NVIDIA STAC-A2 BENCHMARK STAC-A2 Benchmark Developed by banks Macro and micro, performance and accuracy Pricing and Greeks for American

More information

1. INTRODUCTION Graphics 2

1. 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 information

GeoImaging Accelerator Pansharp Test Results

GeoImaging 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 information

Parallel Programming Survey

Parallel 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 information

Autodesk Revit 2016 Product Line System Requirements and Recommendations

Autodesk Revit 2016 Product Line System Requirements and Recommendations Autodesk Revit 2016 Product Line System Requirements and Recommendations Autodesk Revit 2016, Autodesk Revit Architecture 2016, Autodesk Revit MEP 2016, Autodesk Revit Structure 2016 Minimum: Entry-Level

More information

Optimizing a 3D-FWT code in a cluster of CPUs+GPUs

Optimizing 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 information

Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA

Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA Dissertation submitted in partial fulfillment of the requirements for the degree of Master of Technology, Computer Engineering by Amol

More information

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark Data-parallel Acceleration of PARSEC Black-Scholes Benchmark AUGUST ANDRÉN and PATRIK HAGERNÄS KTH Information and Communication Technology Bachelor of Science Thesis Stockholm, Sweden 2013 TRITA-ICT-EX-2013:158

More information

MapReduce on GPUs. Amit Sabne, Ahmad Mujahid Mohammed Razip, Kun Xu

MapReduce on GPUs. Amit Sabne, Ahmad Mujahid Mohammed Razip, Kun Xu 1 MapReduce on GPUs Amit Sabne, Ahmad Mujahid Mohammed Razip, Kun Xu 2 MapReduce MAP Shuffle Reduce 3 Hadoop Open-source MapReduce framework from Apache, written in Java Used by Yahoo!, Facebook, Ebay,

More information

Optimizing Application Performance with CUDA Profiling Tools

Optimizing Application Performance with CUDA Profiling Tools Optimizing Application Performance with CUDA Profiling Tools Why Profile? Application Code GPU Compute-Intensive Functions Rest of Sequential CPU Code CPU 100 s of cores 10,000 s of threads Great memory

More information

Latency and Bandwidth Impact on GPU-systems

Latency and Bandwidth Impact on GPU-systems NTNU Norwegian University of Science and Technology Faculty of Information Technology, Mathematics and Electrical Engineering Department of Computer and Information Science TDT4590 Complex Computer Systems,

More information

22S:295 Seminar in Applied Statistics High Performance Computing in Statistics

22S:295 Seminar in Applied Statistics High Performance Computing in Statistics 22S:295 Seminar in Applied Statistics High Performance Computing in Statistics Luke Tierney Department of Statistics & Actuarial Science University of Iowa August 30, 2007 Luke Tierney (U. of Iowa) HPC

More information

High Performance Computing in CST STUDIO SUITE

High Performance Computing in CST STUDIO SUITE High Performance Computing in CST STUDIO SUITE Felix Wolfheimer GPU Computing Performance Speedup 18 16 14 12 10 8 6 4 2 0 Promo offer for EUC participants: 25% discount for K40 cards Speedup of Solver

More information

The High Performance Internet of Things: using GVirtuS for gluing cloud computing and ubiquitous connected devices

The High Performance Internet of Things: using GVirtuS for gluing cloud computing and ubiquitous connected devices WS on Models, Algorithms and Methodologies for Hierarchical Parallelism in new HPC Systems The High Performance Internet of Things: using GVirtuS for gluing cloud computing and ubiquitous connected devices

More information

GPGPU for Real-Time Data Analytics: Introduction. Nanyang Technological University, Singapore 2

GPGPU for Real-Time Data Analytics: Introduction. Nanyang Technological University, Singapore 2 GPGPU for Real-Time Data Analytics: Introduction Bingsheng He 1, Huynh Phung Huynh 2, Rick Siow Mong Goh 2 1 Nanyang Technological University, Singapore 2 A*STAR Institute of High Performance Computing,

More information

Choosing a Computer for Running SLX, P3D, and P5

Choosing a Computer for Running SLX, P3D, and P5 Choosing a Computer for Running SLX, P3D, and P5 This paper is based on my experience purchasing a new laptop in January, 2010. I ll lead you through my selection criteria and point you to some on-line

More information

Advanced CUDA Webinar. Memory Optimizations

Advanced CUDA Webinar. Memory Optimizations Advanced CUDA Webinar Memory Optimizations Outline Overview Hardware Memory Optimizations Data transfers between host and device Device memory optimizations Summary Measuring performance effective bandwidth

More information

Benchmark Hadoop and Mars: MapReduce on cluster versus on GPU

Benchmark 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 information

NVIDIA Tools For Profiling And Monitoring. David Goodwin

NVIDIA Tools For Profiling And Monitoring. David Goodwin NVIDIA Tools For Profiling And Monitoring David Goodwin Outline CUDA Profiling and Monitoring Libraries Tools Technologies Directions CScADS Summer 2012 Workshop on Performance Tools for Extreme Scale

More information

Optimizing Parallel Reduction in CUDA. Mark Harris NVIDIA Developer Technology

Optimizing 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 information

Mixed Precision Iterative Refinement Methods Energy Efficiency on Hybrid Hardware Platforms

Mixed Precision Iterative Refinement Methods Energy Efficiency on Hybrid Hardware Platforms Mixed Precision Iterative Refinement Methods Energy Efficiency on Hybrid Hardware Platforms Björn Rocker Hamburg, June 17th 2010 Engineering Mathematics and Computing Lab (EMCL) KIT University of the State

More information

ACCELERATION OF SPIKING NEURAL NETWORK ON GENERAL PURPOSE GRAPHICS PROCESSORS

ACCELERATION OF SPIKING NEURAL NETWORK ON GENERAL PURPOSE GRAPHICS PROCESSORS ACCELERATION OF IKING NEURAL NETWORK ON GENERAL PURPOSE GRAPHICS PROCESSORS Thesis Submitted to The School of Engineering of the UNIVERSITY OF DAYTON In Partial Fulfillment of the Requirements for The

More information

PERFORMANCE ENHANCEMENTS IN TreeAge Pro 2014 R1.0

PERFORMANCE ENHANCEMENTS IN TreeAge Pro 2014 R1.0 PERFORMANCE ENHANCEMENTS IN TreeAge Pro 2014 R1.0 15 th January 2014 Al Chrosny Director, Software Engineering TreeAge Software, Inc. achrosny@treeage.com Andrew Munzer Director, Training and Customer

More information

Introduction to CUDA C

Introduction to CUDA C Introduction to CUDA C What is CUDA? CUDA Architecture Expose general-purpose GPU computing as first-class capability Retain traditional DirectX/OpenGL graphics performance CUDA C Based on industry-standard

More information

Alberto 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 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 information

GPU Architectures. A CPU Perspective. Data Parallelism: What is it, and how to exploit it? Workload characteristics

GPU Architectures. A CPU Perspective. Data Parallelism: What is it, and how to exploit it? Workload characteristics GPU Architectures A CPU Perspective Derek Hower AMD Research 5/21/2013 Goals Data Parallelism: What is it, and how to exploit it? Workload characteristics Execution Models / GPU Architectures MIMD (SPMD),

More information

OpenPOWER Outlook AXEL KOEHLER SR. SOLUTION ARCHITECT HPC

OpenPOWER 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 information

High Performance. CAEA elearning Series. Jonathan G. Dudley, Ph.D. 06/09/2015. 2015 CAE Associates

High Performance. CAEA elearning Series. Jonathan G. Dudley, Ph.D. 06/09/2015. 2015 CAE Associates High Performance Computing (HPC) CAEA elearning Series Jonathan G. Dudley, Ph.D. 06/09/2015 2015 CAE Associates Agenda Introduction HPC Background Why HPC SMP vs. DMP Licensing HPC Terminology Types of

More information

Introducing PgOpenCL A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child

Introducing PgOpenCL A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child Introducing A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child Bio Tim Child 35 years experience of software development Formerly VP Oracle Corporation VP BEA Systems Inc.

More information

Overview on Modern Accelerators and Programming Paradigms Ivan Giro7o igiro7o@ictp.it

Overview on Modern Accelerators and Programming Paradigms Ivan Giro7o igiro7o@ictp.it Overview on Modern Accelerators and Programming Paradigms Ivan Giro7o igiro7o@ictp.it Informa(on & Communica(on Technology Sec(on (ICTS) Interna(onal Centre for Theore(cal Physics (ICTP) Mul(ple Socket

More information

SPEEDUP - optimization and porting of path integral MC Code to new computing architectures

SPEEDUP - optimization and porting of path integral MC Code to new computing architectures SPEEDUP - optimization and porting of path integral MC Code to new computing architectures V. Slavnić, A. Balaž, D. Stojiljković, A. Belić, A. Bogojević Scientific Computing Laboratory, Institute of Physics

More information

CUDA Optimization with NVIDIA Tools. Julien Demouth, NVIDIA

CUDA Optimization with NVIDIA Tools. Julien Demouth, NVIDIA CUDA Optimization with NVIDIA Tools Julien Demouth, NVIDIA What Will You Learn? An iterative method to optimize your GPU code A way to conduct that method with Nvidia Tools 2 What Does the Application

More information

Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries

Performance 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 information

PARALLEL JAVASCRIPT. Norm Rubin (NVIDIA) Jin Wang (Georgia School of Technology)

PARALLEL JAVASCRIPT. Norm Rubin (NVIDIA) Jin Wang (Georgia School of Technology) PARALLEL JAVASCRIPT Norm Rubin (NVIDIA) Jin Wang (Georgia School of Technology) JAVASCRIPT Not connected with Java Scheme and self (dressed in c clothing) Lots of design errors (like automatic semicolon

More information

QCD as a Video Game?

QCD 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 information

Lecture 3: Modern GPUs A Hardware Perspective Mohamed Zahran (aka Z) mzahran@cs.nyu.edu http://www.mzahran.com

Lecture 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 information

Hands-on CUDA exercises

Hands-on CUDA exercises Hands-on CUDA exercises CUDA Exercises We have provided skeletons and solutions for 6 hands-on CUDA exercises In each exercise (except for #5), you have to implement the missing portions of the code Finished

More information

ANALYSIS OF RSA ALGORITHM USING GPU PROGRAMMING

ANALYSIS 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 information