'Task graph' presentation slideshows

Programming many core systems Marco Bekooij

Programming many core systems Marco Bekooij. Name? and address?. Outline. Definition many core systems Application domain of many core systems Microsoft Parallel Computing Initiative simplify programming improve quality of service

881 views • 75 slides

Uintah Runtime System Tutorial

857 views • 59 slides

Uintah Runtime System Tutorial

805 views • 59 slides

CS 267: Applications of Parallel Computers Load Balancing

CS 267: Applications of Parallel Computers Load Balancing. James Demmel www.cs.berkeley.edu/~demmel/cs267_Spr09. Outline. Motivation for Load Balancing Recall graph partitioning as load balancing technique Overview of load balancing problems, as determined by Task costs Task dependencies

679 views • 47 slides

OpenMP for Task Decomposition

607 views • 42 slides

CS 194 Parallel Programming Creating and Using Threads

CS 194 Parallel Programming Creating and Using Threads. Katherine Yelick yelick@cs.berkeley.edu http://www.cs.berkeley.edu/~yelick/cs194f07. Parallel Programming Models. Programming model is made up of the languages and libraries that create an abstract view of the machine Control

514 views • 36 slides

COMP Superscalar Tutorial

548 views • 36 slides

Lightweight Concurrent Tasks

430 views • 33 slides

Martino Ruggiero, Michele Lombardi, Michela Milano and Luca Benini University of Bologna,

Communication-Aware Stochastic Allocation and Scheduling Framework for Conditional Task Graphs in Multi-Processor Systems-on-Chip. Martino Ruggiero, Michele Lombardi, Michela Milano and Luca Benini University of Bologna, DEIS - Italy. Outline. Motivations Our approach Problem Model

488 views • 33 slides

Grid Computing

683 views • 29 slides

Presented By: Anirban Basu (20498909 )

369 views • 27 slides

319 views • 24 slides

Deadline Miss Rates of Applications with Stochastic Task Execution Times

361 views • 23 slides

Adaptive Computing on the Grid Using AppLeS

Adaptive Computing on the Grid Using AppLeS. Francine Berman, Richard Wolski, Henri Casanova, Walfredo Cirne, Holly Dail, Marcio Faerman, Silvia Figueira, Jim Hayes, Graziano Obertelli, Jennifer Schopf, Gary Shao, Shava Smallen, Neil Spring, Alan Su, and Dmitrii Zagorodnov.

347 views • 21 slides

Accelerating Machine Learning Applications using Delite

Accelerating Machine Learning Applications using Delite. Anand Atreya, Kevin Brown, George Rossin Stanford University CS315A 1 st June, 2010. What is Machine Learning?. Learning patterns from data Regression Inference (e.g. Loopy Belief Propagation)

317 views • 20 slides

??? Dept. of Electrical Engineering National Cheng Kung University Tainan, Taiwan, R.O.C

Performance Analysis of a JPEG Encoder Mapped To a Virtual MPSoC-NoC Architecture Using TLM 2.0.1. ??? Dept. of Electrical Engineering National Cheng Kung University Tainan, Taiwan, R.O.C. Outline. Abstract Introduction NoC Architecture Encoder Task Graph Task Profiling

303 views • 15 slides

6. Application mapping

6. Application mapping. 6.1 Problem definition 6.2 Scheduling in real-time systems 6.3 Hardware/software partitioning 6.4 Mapping to heterogeneous multi-processors. 6.1 Problem definition. Find: A mapping of applications to processors, Appropriate scheduling techniques (if not fixed), and

318 views • 12 slides

Prithwish Basu ECE Department, Boston University pbasu@bu

183 views • 6 slides

Fully Integrated HMCS System Hager/Okamura/Taylor/Marayong/Dewan/Kragic/Burschka (JHU)

Fully Integrated HMCS System Hager/Okamura/Taylor/Marayong/Dewan/Kragic/Burschka (JHU). Human Factors Evaluation. Virtual fixtures with Steady-Hand Robot. Stereo surface tracking. Task graph execution system. Stereo tool tracking. HMM-based Intent Recognition. = Task 1.

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Problem Definition

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