The University of Georgia Department of Computer Science Department of Computer Science Introducing Parallelism through Sorting Integrating Concepts from

The University of Georgia Department of Computer Science

Introducing Parallelism through Sorting

Integrating Concepts from Parallel and Distributed Computing into the Undergraduate Curriculum

Eileen Kraemer


Overview

We are implementing an integrated approach to the inclusion of concepts in Parallel and Distributed Computing (PDC) in the undergraduate Computer Science curriculum.Timeline:– Spring ‘11

• Intro to Computing and Programming (CSCI1301)

– Fall ‘11• Software Development (CSCI 1302)

– Spring’12• C++ and Unix Systems Programming (CSCI 1730)• Data Structures (CSCI 2720)


Intro to Computing & Programming

Adaptation of this course involves the inclusion of several guest lectures, the first of which uses an evaluation of sorting algorithms to motivate and define parallelism and provide a platform for the discussion of the benefits and limitations of parallel processing.Thursday, April 21st

3 sections~100 students


Introducing Parallelism

Concepts:– definition and rationale of parallel

and distributed computing– parallel sorting


Pre-test

Students were first asked to complete a survey of their knowledge of parallelism, sorting, and time complexity concepts.– See appendix for questions and

representative responses.


Motivating the notion of cost

Human Bubblesort – Roles:

• Processor – Directed the students to change places, according to

algorithm

• Array elements 0 .. 7– held up signs with their value (57,92,77, ...)

• Comparison counter – Given a bag of 50 Tootsie rolls. Had to pay one per

compare.• Swap counter

– Given a bag of 50 chocolate eggs. Had to pay one per swap.

• Step counter – Kept a tally of the number of operations.


Time Complexity Analysis

XXXXXXXX We keep track of the number of

XXXXXXX comparisons & swaps & then

XXXXXX use this sketch to convince

XXXXX ourselves that Bubblesort

XXXX is an n2 algorithm. XXX XX X


Human MergeSort

Students again act out the process of sorting an array of 8 elements.Use Mergesort Time Complexity Analysis performed


MergeSort: time complexity

1 - XXXXXXXX 11 - XXXX 2 – XXXX 12 - XX 3 – XX 13 - X 4 – X 14 -

X 5 - X 15 -

XX 6 - XX 16 -

XX 7 - X 17 -

X 8 - X 18 -

X 9 - XX 19 -

XX10 –XXXX 20 - XXXX 21 –

XXXXXXXX... and we convince ourselves that Mergesort is n

log n


Observation:

The “array elements” spend most of their time waiting around with nothing to doIdea:– use more processors!


Human Parallel MergeSort


Time complexity analysis & discussion

faster in terms of time (steps)– but same number of comparisons– and there’s a limit to the benefit of add’l

processors– and some processors participate only

briefly• sequential elements remain


Conclusion

What affects running time?– size of problem– choice of algorithm– use of parallelism

• nature of problem; percent sequential

– code optimization– leveraging domain knowledge

• characteristics of input set (e.g., already ordered)


Post-survey administered

Currently under analysis


Appendices


Explanation ...

This lecture was presented on 4-21-11 to 100 undergraduate CS students. Compilation and analysis of their responses is under way and will be included in this appendix. Results of analysis will be incorporated into the presentation.


Pre-test Questions & Responses

What does “parallel computing” mean?



What are some of the benefits of parallel computing? That is, why might parallel computing be useful/helpful?



How are these benfits limited, if at all?



What are some of the costs of parallel computing?



Imagine that you have 8 test papers and that you want to put them into order by grade, from lowest to highest. The operations that you can use in performing the sort are:– to compare the values of two papers– to swap the locations of two papers in the

collection

You don’t have any information about the actual test grades or the initial ordering of the test papers.



How many comparisons might you have to perform, roughly?



How many swaps might you have to perform to sort the papers?



Assume that each compare or swap takes one second, an any other work takes 0 time. How long might it take to sort the papers?



Now, imagine that you are asked to sort 1024 papers rather than 8. You can have some friends help you, but you have to pay them $5 to enter the room and $1 for each time step (time required for a compare or swap) during which they are present. Your goal is to minimize the time and the cost of performing the sort.



Explain how you and your friends would share and coordinate the work of sorting the papers.



How quickly can you sort the papers now? Explain, briefly.



How many comparisons are required? Explain, briefly.



How many swaps are required? Explain, briefly.



How many friends would you use? Explain, briefly.



Name all the factors you can think of that might affect the running-time of a sorting program. Then, number the factors in order, using #1 for the factor that has the most effect and higher numbers for factors with lesser effects.

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The University of Georgia Department of Computer Science Department of Computer Science Introducing Parallelism through Sorting Integrating Concepts from