'Linear speedup' diaporamas de présentation

Agenda

Agenda. Day One Traditional Parallel (grid) Computing & HPC Solutions Introduction to Parallel Computing Introduction to Grid Computing Traditional HPC Solutions ProActive Introduction & Installation ProActive overview ProActive installation Guided tour with examples Day Two

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High-Performance Grid Computing and Research Networking

High-Performance Grid Computing and Research Networking. Performance. Presented by Diego Lopez and Javier Munoz Instructor: S. Masoud Sadjadi http://www.cs.fiu.edu/~sadjadi/Teaching/ sadjadi At cs Dot fiu Dot edu. Acknowledgements.

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Performance Engineering in HPC Application Development

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Instructor: Justin Hsia

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Basic Cilk Programming

Basic Cilk Programming. Multithreaded Programming in Cilk L ECTURE 1. Adapted from. Charles E. Leiserson Supercomputing Technologies Research Group Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Multi-core processors. …. P. P. P. $. $.

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Parallel Programming with MPI and OpenMP

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Chapter 7

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Review of Directory Scheme

CS 213 Lecture 6: Multiprocessor 3: Measurements, Crosscutting Issues, Examples, Fallacies & Pitfalls. Review of Directory Scheme. Directory has extra data structure to keep track of state of all cache blocks

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Performance of parallel and distributed systems

Performance of parallel and distributed systems. What is the purpose of measurement? To evaluate a system (or an architecture) To compare two or more systems To compare different algorithms metric to be used? speedup. Workload-Driven Evaluation . Approach

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Calculating Discrete Logarithms

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High performance computing for a family of smooth trajectories using parallel environments

High performance computing for a family of smooth trajectories using parallel environments. Bologna, March 23 - 26, 2004. Gianluca Argentini. Advanced Computing Laboratory. gianluca.argentini@riellogroup.com. Introduction - 1. The company :

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Parallel Applications of the Minimax Algorithm

Parallel Applications of the Minimax Algorithm. Jeff Shelburg Adam Whaley. Overview. Game Trees Minimax Algorithm Minimax Efficiency Improvements Parallel Minimax Parallel Minimax Efficiency Improvements Speedup/Efficiency Results Randomized Best-First Minimax Speedup Results.

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Parallel Computing Explained Parallel Performance Analysis

Parallel Computing Explained Parallel Performance Analysis. Slides Prepared from the CI-Tutor Courses at NCSA http://ci-tutor.ncsa.uiuc.edu/ By S. Masoud Sadjadi School of Computing and Information Sciences Florida International University March 2009. Agenda.

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Parallel Simulated Annealing for EAM potential fitting

Parallel Simulated Annealing for EAM potential fitting. By Tao Xu CS6230 Final Presentation 05/05/05. Outline. Introduction to EAM potential The object/cost function Simulated Annealing Algorithm Synchronous Parallel Simulated Annealing Asynchronous Parallel Simulated Annealing

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Database System Architectures

Database System Architectures. Client-server Database System Parallel Database System Distributed Database System. Wei Jiang. Centralized Systems. CPU. Disk Controller. Printer Controller. Tape-drive Controller. System bus. Memory Controller. Memory. Single-user system

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High Performance Geo-Simulations

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STRATEGIC NAMING: MULTI-THREADED ALGORITHM (Ch 27, Cormen et al.)

STRATEGIC NAMING: MULTI-THREADED ALGORITHM (Ch 27, Cormen et al.). Parallelization Four types of computing: Instruction (single, multiple) per clock cycle Data used (single, multiple) per clock cycle Single Instruction Single Data: Serial computing

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