CS 267: Applications of Parallel Computers

CS 267: Applications of Parallel Computers (Spring 2015, UC Berkeley). Instructor: Professor Jim Demmel. CS 267 is designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets. CS 267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented. (from

Lecture 17 - Structured Grids

Time Lecture Chapters
[00:00:00] 1. Homework Presentation and Class Project Suggestions
[00:23:00] 2. Structured Grids

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Lecture 01 - Introduction
Lecture 02 - Single Processor Machines: Memory Hierarchies and Processor Features
Lecture 03 - Introduction to Parallel Machines and Programming Models
Lecture 04 - Sources of Parallelism and Locality in Simulation
Lecture 05 - Sources of Parallelism and Locality in Simulation (cont.)
Lecture 06 - Shared Memory Programming with Threads and OpenMP, Tricks with Trees
Lecture 07 - Distributed Memory Machines and Programming
Lecture 08 - Partitioned Global Address Space Programming with Unified Parallel C (UPC) and UPC++
Lecture 09 - Performance Debugging Techniques for HPC Applications, Debugging and Optimization Tools
Lecture 10 - Cloud Computing and Big Data Processing
Lecture 11 - An Introduction to CUDA/OpenCL and Graphics Processors (GPUs)
Lecture 12 - Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication
Lecture 13 - Communication Avoiding Algorithms in Dense Linear Algebra
Lecture 14 - Graph Partitioning
Lecture 15 - Sparse Linear Solvers
Lecture 16 - Sparse Iterative Solvers
Lecture 17 - Structured Grids
Lecture 18 - Parallel Graph Algorithms
Lecture 19 - Architecting Parallel Software with Patterns
Lecture 20 - Frameworks in Complex Multiphysics HPC Applications
Lecture 21 - Hierarchical Methods for the N-body Problem
Lecture 22 - Hierarchical Methods for the N-body Problem (cont.)
Lecture 23 - Fast Fourier Transform (FFT) with Applications
Lecture 24 - Big Bang, Big Data, Big Iron: High Performance Computing and the Cosmic Microwave Background
Lecture 25 - Dynamic Load Balancing
Lecture 26 - Modeling and Predicting Climate Change
Lecture 27 - Accelerated Materials Design through High-throughput First-Principles Calculations and Data Mining
Lecture 28 - Big Data, Big Iron and The Future of HPC