Slideshow Transcript

1. Slide 1: Better Living through Computing Algorithms? Stephanie Troeth Montreal Girl Geek Dinner May 28, 2008
2. Slide 2: Project management [This talk is neither about project management ...]
3. Slide 3: Computing [... nor strictly just about computing]
4. Slide 4: Efficiency ...
5. Slide 5: ... through creative problem solving
6. Slide 6: Just for fun. [nothing scientific, or proven, but maybe a basis for a thought experiment]
7. Slide 7: Let’s look at this in two unequal parts • Choosing a couple of known problems, and looking at algorithms to apply • A brief discussion of other algorithms, and perhaps where we can apply them
8. Slide 8: Example issue #1: Time management
9. Slide 9: Other ways you might know • Big rocks vs little rocks • Getting Things Done
10. Slide 10: Big rocks, little rocks • Consider a finite space, such as a jar • Imagine you have big rocks and little rocks • If you fill it with little rocks first, there will be no more space left for the big rocks • If you fill it with big rocks first, you may still fit the little rocks between the gaps
11. Slide 11: Getting things done • Collect - get everything out of your head into your favourite form of “bucket” • Process - trimming off small tasks but allow for way to process bigger jobs • Organize - contextualize things that need doing • Review - make sure your lists are current • Do (!)
12. Slide 12: Key aspects of efficiency • Priorities (though GTD plays down on this) • How tasks are defined • Order of tasks • A way to execute them
13. Slide 13: How do you do it?
14. Slide 14: [at this point, a few people talked about their tips and techniques — “tiny to-do lists”, variations on GTD, what’s worked for them and what hasn’t.]
15. Slide 15: The computer as your bus driver • Priority queues • Schedulers [we discussed bus queues as metaphors]
16. Slide 16: A few algorithms • First In, First Out / Last in, First Out • Shortest Job Next • Shortest Time Remaining • Critical path method • Earliest Deadline First • Round Robin
17. Slide 17: First In, First Out • What comes in first is handled first • What comes in next waits until the first is finished • Basically: first come, first served
18. Slide 18: Last In, First Out • What comes in first is handled last • Every item or task is handled the reverse order they arrived in ... kinda like how you would sort a pile of papers you’ve just stacked together.
19. Slide 19: Round Robin • Gives each item an equal slice of time • Rotates to next item when time is up • Keeps going until all tasks are done
20. Slide 20: Shortest Job Next • Do the shortest job on the queue until it’s done • Pick the next shortest job on the queue gets a lot of things done, but longer jobs won’t get done if you keep adding short jobs
21. Slide 21: Shortest Time Remaining • Do the task that has the smallest amount of time left • When a new task turns up, compare it with the current one that you’re doing, give priority to the task with shortest time ... needs accuracy in time estimation
22. Slide 22: Earliest Deadline First • Do the task that’s closest to its deadline until it’s finished • Then look at your queue for the next item closest to its deadline works okay if you have enough resources to complete all your deadlines ...
23. Slide 23: Critical Path Method • Work out all activities that are required • How long each activity is likely to take? • Which activity depends on which? • Map out the shortest possible time to complete everything by adding up longest essential tasks based on dependencies
24. Slide 24: Example issue #2: Cooking
25. Slide 25: What’s for dinner? • Caesar salad • Lamb roast • Vanilla ice cream with strawberry coulis
26. Slide 26: How do you make sure: • the salad stays fresh • the roast stays warm • the coulis is sufficiently cooled (but not cold) • the ice cream stays frozen • the guests don’t have to wait too long between courses?
27. Slide 27: [at this point the we debated which dish we should begin cooking first, and the finer points on how to make the perfect caesar salad ...]
28. Slide 28: Other ones to get our heads around Divide and conquer Recursively breaking things down into related sub-problems, until each one can be solved directly. Bubble sort Compare pairs of adjacent items in a list, swap if necessary, until no swaps are needed. Travelling salesman problem What is the most economical route if a person were to travel to each city only once (where the distance between cities is known) and return to the home city?
29. Slide 29: Endless fun • Putting away groceries? • Hanging up / putting away laundry? • Cleaning house (bottom up or top down?) • Making the bed? • Applying make-up? • Baking? • Washing dishes? • Watering plants?
30. Slide 30: All that said, we are only human.
31. Slide 31: Thank you.
32. Slide 32: About Stephanie Troeth is someone who has the uncanny knack to make things happen. She likes the challenge of making dreams tangible. http://stephanietroeth.com/ Further Reading • http://www.nist.gov/dads/ • http://www.personal.kent.edu/~rmuhamma/Algorithms/algorithm.html • http://en.wikipedia.org/wiki/Scheduling_%28computing%29 Thanks • Olivier Thereaux • Stephanie Booth • http://flickr.com/photos/christajoy42/2385583808/ • http://flickr.com/photos/30261607@N00/2382070344/ • http://flickr.com/photos/gaetanlee/421949167/