Full Index for Scripted foilset 
We Introduce Computational Science and Driving Forces
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| We give a simple overview of parallel architectures today with distributed, shared or distributed shared memory |
| We describe data, functional and pleasing parallelism |
| We describe principles of parallel programming using atmospheric simulation as an example |
| We describe the growing importance of Java |
We explain pragmatic choices
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1
Overview of Computational Science
2
Abstract of Computational Science Presentation
3
Some Notes on Lecture Technology
4
What is Computational Science ?
5
Synergy of Parallel Computing and Web Internetics as Unifying Principle
6
Basic CPS615 Contact Points
7
Course Organization
8
Material Covered in this Course
9
Structure of CPS615 - II 
10
Basic Structure of Complete CPS615 Base Course on Computational Science Simulation Track -- III
11
What are Parallel and Distributed Computing?
12
Why Parallel Computing?
13
Parallel Computing Technology Rationale
14
Motivating Applications
15
Performance of High End Machines Years 1940-2000
16
Performance of High End Machines Years 1980-2000
17
Peak Supercomputer Performance
18
Some Comments on Simulation and HPCC
19
The Multicomputer: an Idealized Parallel Computer
20
Multicomputer Architecture
21
Multicomputer Cost Model
22
Sequential Memory Structure
23
Parallel Computer Memory Structure
24
Real Parallel Computers Architectures
25
Parallel Computers -- Classic Overview
26
Distributed Memory MIMD Multiprocessor
27
Distributed Memory Machines
28
Distributed Memory Machines -- Notes
29
Shared Memory MIMD Multiprocessor
30
Shared-Memory Machines
31
Shared-Memory Machines -- Notes
32
Distributed Shared Memory (DSM)
33
Distributed Shared Memory Machines
34
Workstation Clusters
35
Parallel Algorithms
36
Data Parallelism in Algorithms
37
Some Illustrative Examples of Parallel Applications!
38
Concurrent Computation as a Mapping Problem -I
39
Concurrent Computation as a Mapping Problem - II
40
Concurrent Computation as a Mapping Problem - III
41
Finite Element Mesh From Nastran
42
A Simple Equal Area Decomposition
43
Decomposition After Annealing
44
Functional Parallelism in Algorithms
45
Structure(Architecture) of Applications - I
46
Structure(Architecture) of Applications - II
47
Multi Server Model for metaproblems
48
Multi-Server Gateway Tier
49
Pleasingly Parallel Algorithms
50
Parallel Languages
51
Data-Parallel Languages
52
Message-Passing Systems
53
A Simple Parallel Programming Model
54
Properties of Programming Model
55
Some Steps in Parallel Programming
56
Partitioning
57
Communication
58
Agglomeration
59
Mapping
60
What is Parallel Architecture?
61
Why Study Parallel Architecture as a computer scientist?
62
Why Study Architecture Today?
63
Inevitability of Parallel Computing
64
Application Trends
65
TPC-C (database transaction processing)
66
Summary of Application Trends
67
Technology Trends -- CPU's
68
General Technology Trends
69
Technology: A Closer Look
70
Clock Frequency Growth Rate
71
Transistor Count Growth Rate
72
Similar Story for Storage
73
Parallel Processing and Society
74
Concurrent Construction of a Wall
75
Quantitative Speed-Up Analysis for Construction of Hadrian's Wall
76
Amdahl's law for Real World Parallel Processing
77
Pipelining --Another Parallel Processing Strategy for Hadrian's Wall
78
Hadrian's Wall Illustrates that the Topology of Processor Must Include Topology of Problem
79
General Speed Up Analysis
80
Nature's Concurrent Computers
81
Comparison of Concurrent Processing in Society and Computing
82
Comparison of The Complete Problem to the subproblems formed in domain decomposition
83
Hadrian's Wall Illustrating an
84
Some Problems are Inhomogeneous Illustrated by:
85
Global and Local Parallelism Illustrated by Hadrian's Wall
86
Parallel I/O Illustrated by
87
Example: Atmosphere Model
88
Atmosphere Model: Numerical Methods
89
Atmosphere Model: Partition
90
Atmosphere Model: Communication
91
Atmosphere Model: Agglomeration
92
Atmosphere Model: Mapping
93
The HPCC Dilemma and its Solution
94
What is Commodity Software
95
The Computing Pyramid
96
Implications of the Computing Pyramid
97
The 3 Roles of Java
98
Why is Java Worth Looking at?
99
What is Java Grande?
100
Java and Parallelism?
101
"Pure" Java Model For Parallelism
102
Java for Scientific Computing Resource
103
Pragmatic Computational Science August 1998 Full WebWisdom URL and this Foilset Search
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