'Data reuse' diaporamas de présentation

Processor Architectures and Program Mapping

Processor Architectures and Program Mapping. Data Memory Management Part b: Loop transformations & Data Reuse. 5KK70 TU/e Henk Corporaal Bart Mesman. Thanks to the IMEC DTSE experts:. Erik Brockmeyer IMEC, Leuven, Belgium and also

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Processor Architectures and Program Mapping

Processor Architectures and Program Mapping. Data Memory Management Part b: Loop transformations & Data Reuse. 5kk10 TU/e Henk Corporaal Jef van Meerbergen Bart Mesman. Thanks to the IMEC DTSE experts:. Erik Brockmeyer IMEC, Leuven, Belgium and also

695 views • 57 slides

DATA MANAGEMENT PLANS & THE DATA CITATION INDEX

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Managing Workflows with the Pegasus Workflow Management System

Managing Workflows with the Pegasus Workflow Management System. Ewa Deelman USC Information Sciences Institute. A collaboration with Miron Livny and Kent Wenger, UW Madison Funded by the NSF OCI SDCI project. deelman@isi.edu http://pegasus.isi.edu. Pegasus: Planning for Execution in Grids.

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Formulating The Problem of

Formulating The Problem of. Compiler-Assisted Cache Replacement. Hongbo Yang. Agenda. Background: Memory hierarchy, ISA with cache hints Problem definition: How should compiler give cache hint to minimize cache miss rate? Case Study for Relationship Between Reference Window and Cache Misses

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Cache Replacement Policy Using Map-based Adaptive Insertion

Cache Replacement Policy Using Map-based Adaptive Insertion. Yasuo Ishii 1,2 , Mary Inaba 1 , and Kei Hiraki 1 1 The University of Tokyo 2 NEC Corporation. Introduction. CORE. Modern computers have multi-level cache system

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CSE 690: GPGPU Lecture 7: Matrix Multiplications

CSE 690: GPGPU Lecture 7: Matrix Multiplications. Klaus Mueller Computer Science, Stony Brook University. Basic Concept. Triple loop. GPU Algorithms. First algorithm: render a rectangle of size NxN represent the matrices as NxN textures each (i,j) is then a fragment

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CSE 690: GPGPU Lecture 7: Matrix Multiplications

CSE 690: GPGPU Lecture 7: Matrix Multiplications. Klaus Mueller Computer Science, Stony Brook University. Basic Concept. Triple loop. GPU Algorithms. First algorithm: render a rectangle of size NxN represent the matrices as NxN textures each (i,j) is then a fragment

294 views • 19 slides

Communication-Minimizing 2D Convolution in GPU Registers

Communication-Minimizing 2D Convolution in GPU Registers . Forrest N. Iandola David Sheffield Michael Anderson P. Mangpo Phothilimthana Kurt Keutzer. forresti@eecs.berkeley.edu. University of California, Berkeley. Overview.

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Data reuse and knowledge sharing

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STEP-NC a new language for CNC machining and measurement

STEP-NC a new language for CNC machining and measurement. Martin Hardwick STEP Tools, Inc. hardwick@steptools.com 518 687-2848 x 306. Why STEP-NC. New information to make machining faster and more accurate Supply chain interoperability On machine feed-speed/tool-wear optimization

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Archive and Access Practices that Support Data Reuse and Transparency

Archive and Access Practices that Support Data Reuse and Transparency. Steven Worley Doug Schuster Bob Dattore National Center for Atmospheric Research. Topics. Data Reuse and Transparency What are these data features? Why are they important? Archiving practices Access practices.

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Data Reuse and Transparency in the Data Lifecycle

Data Reuse and Transparency in the Data Lifecycle. Steven Worley Doug Schuster National Center for Atmospheric Research Boulder, CO USA. Topics. Data Reuse and Transparency What are these data features? Why are they important? Archiving practices Access practices.

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Data Reuse and Sensemaking among Novice Social Scientists

Baltimore, MD, 29 October 2012. Ixchel M. Faniel, Ph.D. Postdoctoral Researcher OCLC Research fanieli@oclc.org. ASIS&T 75 th Annual Meeting, October 26-30, 2012. Data Reuse and Sensemaking among Novice Social Scientists. Adam Kriesberg. Elizabeth Yakel, Ph.D.

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A systolic array for a 2D-FIR filter for image processing

A systolic array for a 2D-FIR filter for image processing. Sebastian Siegel ECE 734 ?. Outline. Why Systolic Arrays (SA)? Design Issues Approach Solution Result. Why Systolic Arrays? (1). 4-level nested do-loop:. Why Systolic Arrays? (2).

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PINNACLE, HIT and Informatics

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