'Parallel threads' diaporamas de présentation

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CUDA GPU Programming

CUDA GPU Programming

CUDA GPU Programming. Shekoofeh A zizi Spring 2012. CUDA is a parallel computing platform and programming model invented by NVIDIA With CUDA, you can send C, C++ and Fortran code straight to GPU, no assembly language required. What is CUDA ?. Development Environment

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872 views • 41 slides
Handling Branches in TLS Systems with Multi-Path Execution

Handling Branches in TLS Systems with Multi-Path Execution

Handling Branches in TLS Systems with Multi-Path Execution. University of Edinburgh http://www.homepages.inf.ed.ac.uk/mc/Projects/VESPA. Polychronis Xekalakis and Marcelo Cintra. Introduction. Power efficiency, complexity and time-to-market reasons lead to CMPs

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497 views • 37 slides
Compositional C++ (CC++)

Compositional C++ (CC++)

Compositional C++ (CC++). Presented by Xiaojin Niu 3/13/2003. Outline. Introduction of CC++ Description of CC++ Performance Issues Summary Reading Assignment. Introduction. Parallel C++ A number of languages and extensions of C++ have emerged trying to make the C++

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333 views • 33 slides
Fast Architecture Evaluation of Heterogeneous MPSoCs by Host-Compiled Simulation

Fast Architecture Evaluation of Heterogeneous MPSoCs by Host-Compiled Simulation

Fast Architecture Evaluation of Heterogeneous MPSoCs by Host-Compiled Simulation. ??? Dept. of Electrical Engineering National Cheng Kung University Tainan, Taiwan, R.O.C 2012.09.10. Abstract.

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415 views • 31 slides
Stanford CS 193G Lecture 15: Optimizing Parallel GPU Performance

Stanford CS 193G Lecture 15: Optimizing Parallel GPU Performance

2010-05-20 John Nickolls. Stanford CS 193G Lecture 15: Optimizing Parallel GPU Performance. Optimizing parallel performance. Understand how software maps to architecture Use heterogeneous CPU+GPU computing Use massive amounts of parallelism Understand SIMT instruction execution

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381 views • 24 slides
Stanford CS 193G Lecture 15: Optimizing Parallel GPU Performance

Stanford CS 193G Lecture 15: Optimizing Parallel GPU Performance

2010-05-20 John Nickolls. Stanford CS 193G Lecture 15: Optimizing Parallel GPU Performance. Optimizing parallel performance. Understand how software maps to architecture Use heterogeneous CPU+GPU computing Use massive amounts of parallelism Understand SIMT instruction execution

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271 views • 24 slides
CS 193G

CS 193G

CS 193G. Lecture 5: Parallel Patterns I. Common Parallel Computing Pattern: Data Blocking. Partition data to operate in well-sized blocks Small enough to be staged in shared memory Assign each data partition to a thread block No different from cache blocking!

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373 views • 23 slides
Bart Haagdorens* , Tim Vermeiren**, Marnix Goossens* bart.haagdorens@vub.ac.be

Bart Haagdorens* , Tim Vermeiren**, Marnix Goossens* bart.haagdorens@vub.ac.be

Improving the Performance of Signature-based Network Intrusion Detection Sensors by Multi-threading. Bart Haagdorens* , Tim Vermeiren**, Marnix Goossens* bart.haagdorens@vub.ac.be *: Vrije Universiteit Brussel **: Alcatel Bell R&I, Antwerp. Outline. Introduction

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380 views • 19 slides
Status of the WMS

Status of the WMS

Status of the WMS. Salvatore Monforte (INFN CT) JRA1 All Hands Meeting 18-20 June 2007, Helsinki. Outline. Details about released patches and relevant bugs fixed Status of testing for certification Status of bulkmm implementation. Patches and bugs fixed.

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265 views • 13 slides

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