'Slow memory' presentation slideshows

Automatic Performance Tuning of Numerical Kernels BeBOP: Berkeley Benchmarking and OPtimization

Automatic Performance Tuning of Numerical Kernels BeBOP: Berkeley Benchmarking and OPtimization. James Demmel EECS and Math UC Berkeley. Katherine Yelick EECS UC Berkeley. Support from NSF, DOE SciDAC, Intel. Performance Tuning Participants. Other Faculty

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Automatic Performance Tuning of Numerical Kernels BeBOP: Berkeley Benchmarking and OPtimization

Automatic Performance Tuning of Numerical Kernels BeBOP: Berkeley Benchmarking and OPtimization. James Demmel EECS and Math UC Berkeley. Katherine Yelick EECS UC Berkeley. Support from NSF, DOE SciDAC, Intel. Performance Tuning Participants. Other Faculty

851 views • 84 slides

Jim Demmel EECS & Math Departments, UC Berkeley demmel@cs.berkeley

Minimizing Communication in Numerical Linear Algebra www.cs.berkeley.edu/~demmel Case Study: Matrix Multiply. Jim Demmel EECS & Math Departments, UC Berkeley demmel@cs.berkeley.edu. Why Matrix Multiplication?. An important kernel in many problems Appears in many linear algebra algorithms

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Single Processor Optimizations Matrix Multiplication Case Study

Single Processor Optimizations Matrix Multiplication Case Study. Kathy Yelick yelick@cs.berkeley.edu www.cs.berkeley.edu/~yelick/cs267_spr07. Outline. Recap from Lecture 2 Memory hierarchy is important to performance Use of simple performance models to understand performance

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Future Computer Advances are Between a Rock (Slow Memory) and a Hard Place (Multithreading)

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Future Computer Advances are Between a Rock (Slow Memory) and a Hard Place (Multithreading)

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Bob Lucas nersc/~dhbailey/cs267

CS 267 Applications of Parallel Computers Lecture 2: Memory Hierarchies and Optimizing Matrix Multiplication. Bob Lucas http://www.nersc.gov/~dhbailey/cs267. Millennium Accounts. To get Millenium accounts, students need to get EECS Instructional Accounts

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James Demmel cs.berkeley/~demmel/cs267_Spr99

CS 267 Applications of Parallel Computers Lecture 2: Memory Hierarchies and Optimizing Matrix Multiplication. James Demmel http://www.cs.berkeley.edu/~demmel/cs267_Spr99. Outline. Understanding Caches Optimizing Matrix Multiplication. Idealized Uniprocessor Model.

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Cache-Oblivious Dynamic Dictionaries with Update/Query Tradeoff

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Complexity Measures for Parallel Computation

Complexity Measures for Parallel Computation. Several possible models!. Execution time and parallelism: Work / Span Model Total cost of moving data: Communication Volume Model Detailed models that try to capture time for moving data: Latency / Bandwidth Model (for message-passing)

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Ayurvedic Supplements To Enhance Memory Power In Aged People

This Power Point Presentation describes about Ayurvedic Supplements To Enhance Memory Power In Aged People

255 views • 11 slides

Cache-Oblivious Dynamic Dictionaries with Update/Query Tradeoffs

114 views • 1 slides