Outline of Material for Test #2

General Algorithm Analysis Knowledge

Be able to compute timings for algorithms using the network model.
Be prepared to use standard communication algorithms (broadcast, reduction, etc) when describing and analyzing parallel algorithms
Be prepared to perform isoefficiency and cost optimality analysis on any of the algorithms covered in the following sections.
In short, you are responsible for the basic analysis knowledge that we have covered in Test#1

Be able to define internal and external sorting algorithms.
What is the compare-exchange operation? How is it implemented in parallel
What is the compare-split operation? How much time does it require?
What is a comparator? How do you draw one? What is a increasing or decreasing comparator and how do you represent it (draw it).
What does it mean to enumerate processors with respect to sorting algorithms? Why is it important for parallel sorting algorithms?

Be able to define a bitonic sequence, bitonic split and bitonic merge.
What is a bitonic merging network? What does it do? What is its size? What is its depth? What does it look like?
What is a bitonic sorting network? What does it do? What is its size? What is its depth? What does it look like?
How do we map a bitonic sorting network onto a hypercube? What does the communication patterns look like?
Be able to derive execution times for the bitonic sorting algorithm on a hypercube architecture. Be able to discuss both mapping one element per processor and many elements per processor.
Be able to perform a scalability analysis of the bitonic sorting algorithm on the hypercube architecture.

Be able to discuss the problem of parallelizing the bubble sort algorithm.
Be able to discuss the odd-even transposition algorithm. How many steps does it have? What is the computation time? Is the algorithm scalable?

Why is it difficult to develop an efficient parallel quick-sort algorithm by simply running independent subproblems in parallel?
What steps are involved in the hypercube quick-sort algorithm?
Why is pivot selection critical for an efficient hypercube sorting algorithm?
What is the scalability of the quick-sort algorithm?
Be prepared to estimate parallel running times for the hypercube based quick-sort algorithm.

What is the bucket sort? What assumptions does it make? What is its serial run time?
What steps are involved in the parallel bucket sort algorithm?
What is a Sample Sort, how does it differ from Bucket Sort?
How do we form a sample set in the Sample Sort? How big is the sample?
How do we sort the Sample in Sample Sort?
What is the performance bottleneck for the Sample Sort?
Be able to estimate running time for these algorithms.
Be able to perform a scalability analysis of these algorithms.