'Global memory' presentation slideshows

Graphics Processing Unit

Graphics Processing Unit. Gert-Jan v.d. Braak Henk Corporaal 5SIA0 , TU/e, 201 8. Background image: Nvidia Tesla P100 with Pascal architecture . Slides by Zhenyu Ye . The GPU-CPU Gap (by NVIDIA ). ref: Tesla GPU Computing Brochure. The GPU-CPU Gap (data).

1.28k views • 112 slides

CUDA Lecture 8 CUDA Memories

731 views • 59 slides

Calcul Reconfigurabil

727 views • 56 slides

Main Memory Map Reduce (M3R)

Main Memory Map Reduce (M3R). VLDB, August 2012 (to appear) Avi Shinnar, David Cunningham, Ben Herta, Vijay Saraswat In collaboration with: Yan Li*, Dave Grove, Mikio Takeuchi**, Salikh Zakirov**, Juemin Zhang * IBM China Research Lab ** IBM Tokyo Research Lab

563 views • 48 slides

100M CUDA GPUs

100M CUDA GPUs. GPU. CPU. CUDA. Heterogeneous Computing. Joy LEE @ NVIDIA. Oil & Gas. Finance. Medical. Biophysics. Numerics. Audio. Video. Imaging. GPU & CUDA Features. Comparing GPU & CPU system. Memory bandwidth Host memory (CPU) ~ 10 GB/s Device memory (GPU) ~ 100 GB/s

646 views • 46 slides

Analyzing Memory Accesses in x86 Executables

Analyzing Memory Accesses in x86 Executables. Gogul Balakrishnan Thomas Reps University of Wisconsin. Crude VSA. VSA. No VSA. Motivation. Basic infrastructure for language-based security buffer-overrun detection information-flow vulnerabilities . . .

666 views • 42 slides

Analyzing Memory Accesses in x86 Executables

Analyzing Memory Accesses in x86 Executables. Gogul Balakrishnan Thomas Reps University of Wisconsin. Crude VSA. VSA. No VSA. Motivation. Basic infrastructure for language-based security buffer-overrun detection information-flow vulnerabilities . . .

513 views • 42 slides

Online Performance Projection for Clusters with Heterogeneous GPUs

Online Performance Projection for Clusters with Heterogeneous GPUs. Lokendra S. Panwar , Ashwin M. Aji , Wu- chun Feng (Virginia Tech, USA) Jiayuan Meng , Pavan Balaji ( Argonne National Laboratory, USA). Diversity in Accelerators. Nov, 2013. Nov, 2008.

525 views • 41 slides

Nvidia CUDA Programming Basics

Nvidia CUDA Programming Basics. Xiaoming Li Department of Electrical and Computer Engineering University of Delaware. Overview. The Programming model The Memory model CUDA API basics A simple example for a kernel function Optimization of Gravit. CUDA Programming Model.

578 views • 36 slides

Energy Efficient GPU Transactional Memory via Space-Time Optimizations

Energy Efficient GPU Transactional Memory via Space-Time Optimizations. Wilson W. L. Fung Tor M. Aamodt. Regular. Irregular. nBody 5M Bodies. 1640s. 5.2s. Why TM for GPU? . Simple Irregular Parallelism on GPU. Other Applications?. Why TM for GPU?. Predictable Dev Time No Deadlock!

479 views • 35 slides

POC: Dr. Daniel B. Weber 559 th SMXS/MXDEF Phone: 405-736-4058

OKLAHOMA CITY AIR LOGISTICS COMPLEX. TEAM TINKER. Using a GPU to Compute the Advection and Computational Mixing Terms of a Numerical Weather Prediction Model Date: 3 October 2012. POC: Dr. Daniel B. Weber 559 th SMXS/MXDEF Phone: 405-736-4058 Email: daniel.weber@tinker.af.mil.

587 views • 34 slides

CUDA

CUDA. Antonyus Pyetro do Amaral Ferreira. The problem. The advent of multicore CPUs and manycore GPUs means that mainstream processor chips are now parallel systems. The challenge is to develop application software that transparently scales its parallelism. A solution.

456 views • 27 slides

Speeding up k -Means by GPUs

Speeding up k -Means by GPUs. YOU LI SUPERVISOR: DR. CHU XIAOWEN CO-SUPERVISOR: PROF. LIU JIMING THURSDAY, MARCH 11, 2010. Outline. Introduction Efficiency of data mining -> GPGPU -> k -means on GPU; Related work Method Research Plan. Efficiency of Data mining.

296 views • 20 slides

16.317 Microprocessor Systems Design I

345 views • 20 slides

Using CUDA for High Performance Scientific Computing

Using CUDA for High Performance Scientific Computing. Dana Schaa NUCAR Research Group Northeastern University. Outline. What is CUDA? Concepts and Terminology Program Design with CUDA Example Programs Ideal Characteristics for Graphics Processing. CUDA and nVidia.

358 views • 19 slides

L11: Jacobi, Tools and Project

252 views • 18 slides

L13: Sorting and OpenGL Interface

L13: Sorting and OpenGL Interface. Administrative. Midterm coming In class March 25, can bring one page of notes Review notes, readings and review lecture Prior exams are posted Design Review Intermediate assessment of progress on project, oral and short In class on April 1

324 views • 17 slides

CUDA Optimizations

CUDA Optimizations. Sathish Vadhiyar Parallel Programming. SIMT Architecture and Warps. Multi-processor creates, manages, schedules and executes threads in groups of 32 parallel threads called warps Threads in a warp start at the same program address

322 views • 14 slides

Fast Forwarding Table Lookup Exploiting GPU Memory Architecture

Fast Forwarding Table Lookup Exploiting GPU Memory Architecture. Date:100/12/14 Publisher:IEEE ICTC 2010 Author:Youngjun Lee,Minseon Jeong,Sanghwan Lee,Eun -Jin Im Kookmin University. Introduction.

283 views • 14 slides

Monitoring Update

Monitoring Update. David Lawrence, JLab Feb. 20, 2014. (Data flows from left to right). * n.b. all L3 machines connected via InfiniBand. Farm Controller CODA Component. Farm Manager process will be a full CODA component (i.e. required component of the DAQ system)

214 views • 9 slides