1. Recognizing Potential Parallelism Introduction to Parallel Programming Part 1

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3. What Is Parallel Computing? Attempt to speed solution of a particular task by 1. Dividing task into sub-tasks 2. Executing sub-tasks simultaneously on multiple processors Successful attempts require both 1. Understanding of where parallelism can be effective 2. Knowledge of how to design and implement good solutions

4. Clock Speeds Have Flattened Out Problems caused by higher speeds Excessive power consumption Heat dissipation Current leakage Power consumption critical for mobile devices Mobile computing platforms increasingly important Retail laptop sales now exceed desktop sales Laptops may be 35% of PC market in 2007

5. Multi-core Architectures Potential performance = CPU speed  # of CPUs Strategy: Limit CPU speed and sophistication Put multiple CPUs (“cores”) on a single chip Potential performance the same

6. Concurrency vs. Parallelism Concurrency: two or more threads are in progress at the same time: Parallelism: two or more threads are executing at the same time Multiple cores needed Thread 1 Thread 2 Thread 1 Thread 2

7. Improving Performance Use parallelism in order to improve turnaround or throughput Examples Automobile assembly line Each worker does an assigned function Searching for pieces of Skylab Divide up area to be searched US Postal Service Post office branches, mail sorters, delivery

8. Turnaround Complete single task in the smallest amount of time Example: Setting a dinner table One to put down plates One to fold and place napkins One to place utensils One to place glasses

9. Throughput Complete more tasks in a fixed amount of time Example: Setting up banquet tables Multiple waiters each do separate tables Specialized waiters for plates, glasses, utensils, etc.

10. Methodology Study problem, sequential program, or code segment Look for opportunities for parallelism Try to keep all processors busy doing useful work

11. Ways of Exploiting Parallelism Domain decomposition Task decomposition

12. Domain Decomposition First, decide how data elements should be divided among processors Second, decide which tasks each processor should be doing Example: Vector addition

13. Domain Decomposition Large data sets whose elements can be computed independently Divide data and associated computation among threads Example: Grading test papers Multiple graders with same key What if different keys are needed?

14. Domain Decomposition Find the largest element of an array

15. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

16. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

17. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

18. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

19. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

20. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

21. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

22. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

23. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

24. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

25. Domain Decomposition Find the largest element of an array Core 0Core 1Core 2Core 3

26. Task (Functional) Decomposition First, divide tasks among processors Second, decide which data elements are going to be accessed (read and/or written) by which processors Example: Event-handler for GUI

27. Task Decomposition Divide computation based on natural set of independent tasks Assign data for each task as needed Example: Paint-by-Numbers Painting a single color is a single task Number of tasks = number of colors Two artists: one does even, other odd 1 1 1 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 55 5 5 5 5 5 5 5 5 5 5 5 5 3 6 6 7 9 8 3 8 3 3 8 8 9 1 10 7 6 11

28. Task Decomposition f() s() r() q() h() g()

29. Task Decomposition f() s() r() q() h() g() Core 0 Core 2 Core 1

30. Task Decomposition f() s() r() q() h() g() Core 0 Core 2 Core 1

31. Task Decomposition f() s() r() q() h() g() Core 0 Core 2 Core 1

32. Task Decomposition f() s() r() q() h() g() Core 0 Core 2 Core 1

33. Task Decomposition f() s() r() q() h() g() Core 0 Core 2 Core 1

34. Recognizing Sequential Processes Time is inherently sequential Dynamics and real-time, event driven applications are often difficult to parallelize effectively Many games fall into this category Iterative processes The results of an iteration depend on the preceding iteration Audio encoders fall into this category Pregnancy is inherently sequential Adding more people will not shorten gestation

35. Summary Clock speeds will not increase dramatically Parallelism takes full advantage of multi-core processors Improve application turnaround or throughput Two methods for implementing parallelism Domain Decomposition Task Decomposition