'Sparse matrix' diaporamas de présentation

Katherine Yelick Lawrence Berkeley National Laboratory and U. C. Berkeley, EECS Dept.

Performance Understanding, Prediction, and Tuning at the Berkeley Institute for Performance Studies (BIPS). Katherine Yelick Lawrence Berkeley National Laboratory and U. C. Berkeley, EECS Dept. November 2004. Outline. Motivation for Automatic Performance Tuning

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A Vision for Integrating Performance Counters into the Roofline model

A Vision for Integrating Performance Counters into the Roofline model. Samuel Williams 1,2 samw@cs.berkeley.edu with Andrew Waterman 1 , Heidi Pan 1,3 , David Patterson 1 , Krste Asanovic 1 , Jim Demmel 1 1 University of California, Berkeley 2 Lawrence Berkeley National Laboratory

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Data Structures

Data Structures. Chapter 4: Linked Lists. Singly Linked List. data. link(pointer, address) ? -1 ???? (null pointer). first = 2 header, external pointer. 2 5 6 1. **. first. null. node. data link. Operation on the First Node. first.

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Henry Neeman, University of Oklahoma Charlie Peck, Earlham College Tuesday October 11 2011

Parallel Programming & Cluster Computing Grab Bag: Scientific Libraries, I/O Libraries, Visualization. Henry Neeman, University of Oklahoma Charlie Peck, Earlham College Tuesday October 11 2011. Outline. Scientific Computing Pipeline Scientific Libraries I/O Libraries

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Supercomputing in Plain English Grab Bag: Scientific Libraries, I/O Libraries, Visualization

Supercomputing in Plain English Grab Bag: Scientific Libraries, I/O Libraries, Visualization. PRESENTERNAME PRESENTERTITLE PRESENTERDEPARTMENT PRESENTERINSTITUTION DAY MONTH DATE YEAR. Your Logo Here. Slides by Henry Neeman, University of Oklahoma. Outline. Scientific Computing Pipeline

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OLAP in DWH

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A SEMI-AUTOMATED SYSTEM FOR NANOVOLUME PLUG-BASED CRYSTALLIZATION

PSI-2 Bottlenecks Meeting, Natcher Conference Center April 14-16, 2008. A SEMI-AUTOMATED SYSTEM FOR NANOVOLUME PLUG-BASED CRYSTALLIZATION . Cory Gerdts, Ph.D. April 15, 2008. ATCG3D is a Specialized PSI-2 Center Funded by the NIGMS and NCRR, Grant # U54-GM074961-03.

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Adaptable benchmarks for register blocked sparse matrix-vector multiplication

Adaptable benchmarks for register blocked sparse matrix-vector multiplication. Berkeley Benchmarking and Optimization group (BeBOP) Hormozd Gahvari and Mark Hoemmen Based on research of: Eun-Jin Im Rich Vuduc. Outline. Sparse matrix-vector multiplication

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CS4961 Parallel Programming Lecture 8: Introduction to Threads and Data Parallelism in OpenMP Mary Hall September 17, 2

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Solving Linear Systems (Numerical Recipes, Chap 2)

Solving Linear Systems (Numerical Recipes, Chap 2). Jehee Lee Seoul National University. A System of Linear Equations. Consider a matrix equation There are many different methods for solving this problem We will not discuss how to solve it precisely

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The Future of Numerical Linear Algebra Libraries: Automatic Tuning of Sparse Matrix Kernels The Next LAPACK and ScaLAPAC

James Demmel Computer Science and Mathematics Departments UC Berkeley SciDAC 2005. The Future of Numerical Linear Algebra Libraries: Automatic Tuning of Sparse Matrix Kernels The Next LAPACK and ScaLAPACK. Richard Vuduc (LLNL), James Demmel, Katherine Yelick

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STAR-P: High Productivity Parallel Computing

STAR-P: High Productivity Parallel Computing. David Cheng, Ron Choy, Alan Edelman Massachusetts Institute of Technology John R. Gilbert and Viral Shah University of California at Santa Barbara. Graph Algorithms and Sparse Matrix Land. Birth of Interactive Supercomputing.

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EE 616 Computer Aided Analysis of Electronic Networks Lecture 4

EE 616 Computer Aided Analysis of Electronic Networks Lecture 4. Instructor: Dr. J. A. Starzyk, Professor School of EECS Ohio University Athens, OH, 45701. 09/16/2005. Note: some materials in this lecture are from the notes of UC-berkeley. Review and Outline.

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Computation on Graphs: Partitioning, Maximal Independent Set, etc.

Computation on Graphs: Partitioning, Maximal Independent Set, etc. P0 P1 P2 P3. x. P0 P1 P2 P3. y. Parallel sparse matrix-vector product. Lay out matrix and vectors by rows y( i ) = sum(A( i,j )*x(j)) Only compute terms with A( i,j ) ? 0 Algorithm Each processor i :

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NUMERICAL INVESTIGATION OF WAVE EFFECTS IN HIGH-FREQUENCY CAPACITIVELY COUPLED PLASMAS*

NUMERICAL INVESTIGATION OF WAVE EFFECTS IN HIGH-FREQUENCY CAPACITIVELY COUPLED PLASMAS* Yang Yang and Mark J. Kushner Department of Electrical and Computer Engineering Iowa State University, Ames, IA 50011 yangying@iastate.edu mjk@iastate.edu http://uigelz.ece.iastate.edu October 2007.

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Data Structures & Algorithm Analysis

Data Structures & Algorithm Analysis. Arrays. Arrays. Array: a set of pairs ( index and value ) data structure For each index, there is a value associated with that index. representation (possible) implemented by using consecutive memory.

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CSCE 3110 Data Structures & Algorithm Analysis

CSCE 3110 Data Structures & Algorithm Analysis. YOGESH KOLHE, PGT(Comp.Sci.). Arrays. Array: a set of pairs ( index and value ) data structure For each index, there is a value associated with that index. representation (possible) implemented by using consecutive memory.

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Integrating SciDAC Modules with Community Application Codes

Integrating SciDAC Modules with Community Application Codes. USQCD Collaboration SciDAC-II Review C. DeTar, Univ Utah. Outline. Community code bases Code design and structure Integration with SciDAC suite Future directions. Community Code Bases. Chroma ( Jlab ) (Edwards talk)

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