'Device memory' diaporamas de présentation

KeyStone Multicore Navigator

KeyStone Multicore Navigator. KeyStone Training Multicore Applications Literature Number: SPRP812. Objectives. The purpose of this lesson is to enable you to do the following: Explain the advantages of using Multicore Navigator.

1.03k views • 64 slides

HPC Computing with CUDA and Tesla Hardware

HPC Computing with CUDA and Tesla Hardware. Tim Lanfear, NVIDIA. What is GPU Computing?. Heterogeneous Computing: CPU and GPU. PCIe bus. GPU. x86. Control. ALU. ALU. ALU. ALU. DRAM. Cache. DRAM. Low Latency or High Throughput?. CPU

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An Introduction to CUDA and Manycore Graphics Processors

An Introduction to CUDA and Manycore Graphics Processors. Bryan Catanzaro, UC Berkeley. Universal Parallel Computing Research Center University of California, Berkeley. Overview. Terminology: Multicore , Manycore , SIMD The CUDA Programming model Mapping CUDA to Nvidia GPUs

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Using The CUDA Programming Model

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Leveraging GPUs for Application Acceleration Dan Ernst Cray, Inc.

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Thrust

Thrust. Jared Hoberock and Nathan Bell. Diving In. #include <thrust/host_vector.h> #include <thrust/device_vector.h> #include <thrust/sort.h> int main( void ) { // generate 16M random numbers on the host thrust:: host_vector < int > h_vec(1 << 24);

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Software Pipelined Execution of Stream Programs on GPUs

Software Pipelined Execution of Stream Programs on GPUs. Abhishek Udupa et. al. Indian Institute of Science, Bangalore 2009. Abstract. Describe the challenges in mapping StreamIt to GPUs Propose an efficient technique to software pipeline the execution of stream programs on GPUs

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Thrust & Curand

Thrust & Curand. Dr. Bo Yuan E-mail: yuanb@sz.tsinghua.edu.cn. Outline. Thrust Basic Work with Thrust General Types of Thrust Fancy Iterators of Thrust Curand Library. Thrust Basic. What is Thrust? Containers Iterators Interaction with raw pointers Namespace. What is Thrust?.

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MapReduce

MapReduce. Concurrency for data-intensive applications. MapReduce. Jeff Dean. Sanjay Ghemawat. Motivation. Application characteristics Large/massive amounts of data Simple application processing requirements Desired portability across variety of execution platforms. Execution platforms.

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Distributed Video Coding and Its Application

Distributed Video Coding and Its Application. Abhik Majumdar, Rohit Puri, Kannan Ramchandran, and Jim Chou. Presented by Lei Sun. Introduction(1/3).

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Author : Yongchao Liu, Douglas L Maskell, Bertil Schmidt Publisher: BMC Research Notes, 2009

CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units. Author : Yongchao Liu, Douglas L Maskell, Bertil Schmidt Publisher: BMC Research Notes, 2009 Speaker : De Yu Chen Data: 2011/4/20. Outline. Introduction Smith-Waterman Algorithm

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A device-driver for Video Memory

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Multi-core Acceleration of NWP

Multi-core Acceleration of NWP. John Michalakes, NCAR John Linford, Virginia Tech Manish Vachharajani, University of Colorado Adrian Sandu, Virginia Tech HPC Users Forum, September 10, 2009. Outline. WRF and multi-core overview Cost breakdown and kernel repository Two cases Path forward.

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First CUDA Program

First CUDA Program. First C program. First CUDA program. # include " stdio.h " int main() { printf ("Hello, world "); return 0; }. #include < cuda.h > #include < stdio.h > __global__ void kernel (void) { } int main (void) { kernel <<< 1, 1 >>> (); printf ("Hello World! ");

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Multipattern String Matching On A GPU

Multipattern String Matching On A GPU. Author : Xinyan Zha , Sartaj Sahni Publisher: 16th IEEE Symposium on Computers and Communications Presenter: Ye- Zhi Chen Date: 2011/11/1. Introduction.

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Parallelization and Characterization of Pattern Matching using GPUs

Parallelization and Characterization of Pattern Matching using GPUs. Author : Giorgos Vasiliadis ? Michalis Polychronakis ? Sotiris Ioannidis Publisher: IEEE IISWC 2011 Presenter: Ye- Zhi Chen Date: 2012/02/15. Introduction.

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GPU programming: CUDA

GPU programming: CUDA. Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin ( Negrut ), North Carolina Charlotte ( Wikinson /Li) and NCSA ( Kindratenko ). CUDA.

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Crack Propagation in Brittle Solids

Crack Propagation in Brittle Solids. CUDA Simulation Benjy Kessler. Background. Given a brittle substance with a crack in it. The goal is to study how the crack propagates in the substance as a function of time.

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Memory Mapped Files

Memory Mapped Files. Using the Linux mechanism for direct access to device data. Typical file access senario. Use open(), lseek(), read(), write(), close() Each involves two privilege-transitions At most 4096 bytes transferred each time Inefficient for non-sequential data access.

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