Computational Research Topics: Introduction to Parallel and Distributed Computing Presentation by Jonathan Schaeffer April 29, 1999.
Slide Show INDEX:

1: Training Program
2: Training Program
3: Introduction
4: Save Programming
5: Save Programming
6: Computational Research
7: Grand Challenges
8: Powerful Computers
9: Speed and Storage
10: Solution
11: Why Not Use a PC?
12: Super? Computing
13: Parallel Computing
14: SGI Origin 2000
15: Distributed Computing
16: Alpha Cluster
17: The Dilemma
18: One Perspective
19: Sequential Thinking
20: The Reality Is
21: Do You Need Parallelism?
22: Resistance to Parallelism
23: A Solution!
24: Speedup
25: Speedup
26: Which is Better?
27: It Depends!
28: Warning!
29: Superlinear Speedups
30: Amdahl's Law (1)
31: Amdahl's Law (2)
32: Amdahl's Law (3)
33: Gustafson's Law
34: Scalability
35: Starting Out...
36: Starting Out...
37: Granularity (1)
38: Granularity (2)
39: Granularity (3)
40: Matrix Multiply (1)
41: Matrix Multiply (2)
42: Matrix Multiply (3)
43: Architectures
44: Program Design
45: Programming Models
46: Identify Parallelism
47: Choose Right Algorithm
48: Vector Processing (1)
49: Vector Processing (2)
50: Vector Processing (3)
51: Distributed Memory (1)
52: Distributed Memory (2)
53: Communication
54: Synchronization
55: Message Passing (1)
56: Message Passing (2)
57: Master/Slave (1)
58: Master/Slave (2)
59: Pitfall: Deadlock
60: Pitfall: Deadlock
61: Pitfall: Load Balancing
62: Shared Memory (1)
63: Shared Memory (2)
64: Communication
65: Synchronization
66: Pitfall: Shared Data Access
67: OpenMP (1)
68: OpenMP (2)
69: OpenMP (3)
70: HPF (1)
71: HPF (2)
72: Other Models
73: Other Issues
74: Fault Tolerance
75: Conclusions
76: Reminders
77: We Want You!
78: Contact