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This module was created as an entry for the 2008-'09 Open Education Cup: High Performance Computing competition. The competition was supervised by Dr. Jan Erik Odegard, Executive Director of the Ken Kennedy Institute for Information Technology at Rice University. It was submitted to the "Parallel Algorithms and Applications" category and specifically designed as an introduction to the subject targeting intermediate grade school students to collegiate undergraduates who have little knowledge of High Performance Computing (HPC).
This module received the "Best Module" award for the "Parallel Algorithms and Applications" category which included a US $500 prize.
Those who reviewed the entries for the competition made some suggestions for improvement and most have been incorporated into this revised edition of the module. As always; my thanks to them and all others who make suggestions for improving educational materials.
Kenneth Leroy Busbee
Grouping multiple computers or multiple computer processors to accomplish a task quicker is referred to as High Performance Computing (HPC). We want to explain how this is accomplished using parallel programming algorithms or concepts.
We are going to start our explanation by giving two simple examples.
After eating all you can, you toss your chicken leg bone out of the car window (shame on you for trashing up the highway), but in short order an ant finds your tossed chicken bone. One single ant could bite off the left over on the bone and transport it to the colony, one bite at a time; but, it might take him 1 whole day (24 hours) of work. But, what if he gets help? He signals some buddies and being a small colony of ants they allocate a total of 10 ants to do the task. Ten times the workers take one tenth the time. The ten ants do the task in 2 hours and 24 minutes.
I toss another bone out the window. An ant finds it and the colony allocates 50 ants to do the task of picking the bone clean. In less than 30 minutes (28.8 to be exact) the 50 ants working in parallel complete the task.
One painter might take 8 hours to paint the exterior of an average sized house. But, if he can put a crew of 10 painters working simultaneously (or in other words in parallel) it takes only 48 munities. What about a crew of 50 painters assuming that they can do work and not get in the way of each other; well how about less than 10 minutes (9.6 to be exact).
Now let's make sure we understand that the same amount of work was done in the examples given. The work was only completed in a shorter amount of time because we put more workers on the task. Not all tasks can be divided up in this way, but when it can be divided between multiple workers, we can take advantage of the workers doing their sub part of the task in parallel. Let’s look at another example.
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