Given by Geoffrey C. Fox at CPS615 Basic Simulation Track for Computational Science on Fall Semester 95. Foils prepared 8 Nov 1995
Outside Index
Summary of Material
This starts with a discussion of Parallel Computing using analogies from nature |
It uses foils and material from CSEP chapter on Computer Architecture to discuss how and why to build a parallel computer including synchronization memory structure and network issues |
SIMD and MIMD Architectures with a brief comparison of workstation networks with closely coupled systems |
A look to the future is based on results from Petaflops workshop |
Outside Index Summary of Material
Geoffrey Fox |
NPAC |
Room 3-131 CST |
111 College Place |
Syracuse NY 13244-4100 |
This starts with a discussion of Parallel Computing using analogies from nature |
It uses foils and material from CSEP chapter on Computer Architecture to discuss how and why to build a parallel computer including synchronization memory structure and network issues |
SIMD and MIMD Architectures with a brief comparison of workstation networks with closely coupled systems |
A look to the future is based on results from Petaflops workshop |
Geoffrey Fox |
NPAC |
Room 3-131 CST |
111 College Place |
Syracuse NY 13244-4100 |
This is designed to augment the Fosdick and Jessup online resource called |
A Review of Selected Topics from Numerical Analysis |
situated at: http://www.cs.colorado.edu/95-96/courses/materials.hpsc.html |
We focus on additional discussion of eigenvectors and eigenvalues which are used in CPS615 in discussing convergence of iterative PDE solvers |
Also have small discussion of "functional analysis" with differential operators |
Geoffrey Fox |
NPAC |
Room 3-131 CST |
111 College Place |
Syracuse NY 13244-4100 |
This derives the finite element method for a simple two dimensional Laplacian with triangular elements |
We use this to motivate conjugate gradient as a variant of steepest descent for variational principle underlying FEM |
We discuss preconditioning, parallelism and convergence of general conjugate gradient method |