1. Parallel algorithm design
Group 2 - Lukasz Wisniewski, Dave Edwards, Junjie Tang

2. Things to take into consideration
Partitioning - how and what to split
Communication - how and what tasks (processes) communicate
Agglomerate (granularity) - how big each communication or computation chunk is
Map - how and when is data or computation split

3. Algorithm models Data-parallel model - data is split between processes
Task graph model - split data or computations into a graph
Work pool model - dynamically add to and assign tasks from a pool (i.e. stack)
Master-slave model - master splits work between workers
Pipeline model - each processor does different part of computation on data
Hybrid models - different models combined

4. Algorithm decision tree

5. References
I. T. Foster, Designing and Building Parallel Programs, Addison-Wesley, 1995
A. Grama, A. Gupta, G. Karypis, and V. Kumar, Introduction to Parallel Computing, 2nd. ed., Addison-Wesley, 2003
T. G. Mattson and B. A. Sanders and B. L. Massingill, Patterns for Parallel Programming, Addison-Wesley, 2005