'Level parallelism' diaporamas de présentation

Synchronization 3 and GPUs

Synchronization 3 and GPUs. CS 3410, Spring 2014 Computer Science Cornell University. See P&H Chapter: 6.7. Administrivia. Next 3 weeks Prelim2 Thu May 1 st : 7:30-9:30 Olin 155: Netid [a-g]* Uris G01: Netid [h-z]* Proj3 tournament: Mon May 5 5pm-7pm (Pizza!)

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Instructor: Justin Hsia

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Performance Optimization of HPC Applications on Multi- and Manycore Processors

Performance Optimization of HPC Applications on Multi- and Manycore Processors. Samuel Williams 1 Kamesh Madduri 1 , Khaled Ibrahim 1 , Jonathan Carter 1 , Leonid Oliker 1 , John Shalf 1 , David Donofrio 1 , Kaushik Datta 2 , Vasily Volkov 2 , Stephane Ethier 3 , Jens Kreuger 4.

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Task Parallelism and Task Superscalar Processing

Task Parallelism and Task Superscalar Processing. A preview of work done in Barcelona Supercomputing Center (and other places) Collected by Alon Naveh, June-2010 Seminar in VLSI Architecture (048879). Content. Preface Incentives for Task-Superscalar Alternative solutions for ILP

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On Power and Multi-Processors

On Power and Multi-Processors. Finishing up power issues and how those issues have led us to multi-core processors. Introduce multi-processor systems. Stuff upcoming soon. 3/24: HW4 due 3/26: MS2 due 3/28: MS2 meetings (sign up posted on 3/24) 4/2: Quiz. Power from last time.

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ATLAS A Scalable and High-Performance Scheduling Algorithm for Multiple Memory Controllers

ATLAS A Scalable and High-Performance Scheduling Algorithm for Multiple Memory Controllers. Yoongu Kim Dongsu Han Onur Mutlu Mor Harchol-Balter. Motivation. Modern multi-core systems employ multiple memory controllers Applications contend with each other in multiple controllers

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Neither More Nor Less: Optimizing Thread-level Parallelism for GPGPUs

Neither More Nor Less: Optimizing Thread-level Parallelism for GPGPUs. Onur Kay ? ran , Adwait Jog , Mahmut Kandemir, Chita R. Das . GPU Computing. GPUs are known for providing high thread-level parallelism (TLP). Uni -processors. Multi-cores. Many-cores, GPUs. . Core. Core. Core.

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Scheduling of Parallelized Synchronous Dataflow Actors

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Uncompressing a Projection Index in CUDA

Uncompressing a Projection Index in CUDA. Eduardo Gutarra Velez. Prefix Sum (Scan ). Many uses for prefix sum but our use will be to uncompress a previously compressed index, that will be sent to memory. A3B1C7 AAABCCCCCCC Source:. Introduction.

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Matrix Multiplication in CUDA

Matrix Multiplication in CUDA. A case study. Matrix Multiplication: A Case Study. Matrix multiplication illustrates many of the basic features of memory and thread management in CUDA Usage of thread/block IDs Memory data transfer between host and device Motivates some performance issues:

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Concurrency

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Apr 9, 2012

MemGuard: Memory Bandwidth Reservation System for Efficient Performance Isolation in Multi-core Platforms. Apr 9, 2012 Heechul Yun + , Gang Yao + , Rodolfo Pellizzoni * , Marco Caccamo + , Lui Sha + + University of Illinois at Urbana-Champaign * University of Waterloo.

531 views • 34 slides

Taking CUDA to Ludicrous Speed

Taking CUDA to Ludicrous Speed. Getting Righteous Performance from your GPU. Performance: How Much Is Enough? (CPU Edition). Could I be getting better performance? Probably a little bit. Most of the performance is handled in HW How much better?

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Taking CUDA to Ludicrous Speed Getting Righteous Performance from your GPU Dan Ernst Cray, Inc.

Taking CUDA to Ludicrous Speed Getting Righteous Performance from your GPU Dan Ernst Cray, Inc. Performance: How Much Is Enough? (CPU Edition). Could I be getting better performance? Probably a little bit. Most of the performance is handled in HW How much better?

372 views • 22 slides

Parallel and Distributed Computing Early ( a nd Often)

Parallel and Distributed Computing Early ( a nd Often). First NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar-11) May 16 th , 2011. Steven Bogaerts (speaker), Kyle Burke, Brian Shelburne Department of Mathematics & Computer Science

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An Adaptive Task Creation Strategy for Work-Stealing Scheduling

An Adaptive Task Creation Strategy for Work-Stealing Scheduling. Lei Wang , Huimin Cui, Yuelu Duan , Fang Lu, Xiaobing Feng , Pen-Chung Yew. ICT, Chinese Academy of Sciences, China University of Minnesota, U.S.A. Forecast . Adaptive task granularity. fine-grained parallelism. tasks.

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Scaling Up Graphical Model Inference

Scaling Up Graphical Model Inference. Graphical Models. View observed data and unobserved properties as random variables Graphical Models: compact graph-based encoding of probability distributions (high dimensional, with complex dependencies)

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Multi-threaded RTOS

Multi-threaded RTOS . How Multi-threading can increase on-chip parallelism. Outline. Introduction Multi-threading models Architectures of multi-threaded processors Simultaneous multi-threading and multi-processors Cache design Examples of Multi-threaded environments Conclusions.

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CSCE 313: Embedded Systems Multiprocessor Systems

CSCE 313: Embedded Systems Multiprocessor Systems. Instructor: Jason D. Bakos. Multiprocessor Systems. SOPC Builder allows you to add multiple CPUs to your design The CPUs can share memories and other system components

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CompSci 230 Software Construction

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