CSE8380 Parallel and Distributed Processing Presentation

1. CSE8380 Parallel and Distributed ProcessingPresentation Hong Yue Department of Computer Science & Engineering Southern Methodist University

2. Parallel Processing Multianalysis--- Compare Parallel Processing with Sequential Processing

3. Why did I select this topic?

4. Outline • Definition • Characteristics of Parallel Processing and Sequential Processing • Implementation of Parallel Processing and Sequential Processing • Performance of Parallel Processing and Sequential Processing • Parallel Processing Evaluation • Major Application of parallel processing

5. Definition • Parallel Processing Definition Parallel Processing refers to the simultaneous use of multiple processors to execute the same task in order to obtain faster results. These processors either communicate each other to solve a problem or work completely independent, under the control of another processor which divides the problem into a number of parts to other processors and collects results from them.

6. Definition .2 • Sequential Processing Definition Sequential processing refers to a computer architecture in which a single processor carries out a single task by series of operations in sequence. It is also called serial processing.

7. Characteristics of Parallel Processing and Sequential Processing • Characteristics of Parallel Processing ● Each processor can perform tasks concurrently. ● Tasks may need to be synchronized. ● Processors usually share resources, such as data, disks, and other devices.

8. Characteristics of Parallel Processing and Sequential Processing .2 • Characteristics of Sequential Processing ● Only one single processor performs task. ● The single processor performs a single task. ● Task is executed in sequence.

9. Implementation of parallel processing and sequential processing • Executing single task In sequential processing, the task is executed as a single large task. In parallel processing, the task is divided into multiple smaller tasks, and each component task is executed on a separate processor.

10. Implementation of parallel processing and sequential processing.2

11. Total Elapsed Time Processor 1 Processor 2 Processor 3 Processor 4 Processor 5 Processor 6 Processor 7 Component task (runtime) Figure 2 Parallel Processing: Executing Component Tasks in Parallel Implementation of parallel processing and sequential processing .3

12. Implementation of parallel processing and sequential processing.4 • Executing multiple independent task ● In sequential processing, independent tasks compete for a single resource. Only task 1 runs without having to wait. Task 2 must wait until task 1 has completed; task 3 must wait until tasks 1 and 2 have completed, and so on.

13. Implementation of parallel processing and sequential processing .5 • Executing multiple independent task ● By contrast, in parallel processing, for example, a parallel server on a symmetric multiprocessor, more CPU power is assigned to the tasks. Each independent task executes immediately on its own processor: no wait time is involved.

14. Total Elapsed Time Processor 1 Processor 2 Processor 3 Processor 4 Processor 5 Processor 6 Processor 7 Task (runtime) Wait Figure 3 Sequential Processing of Multiple Independent Tasks Implementation of parallel processing and sequential processing .6

15. Processor 1 Processor 2 Processor 3 Processor 4 Processor 5 Processor 6 Processor 7 Total Elapsed Time Task (runtime) Figure 4 Parallel Processing: Executing Independent Tasks in Parallel Implementation of parallel processing and sequential processing .7

16. Performance of parallel processing and sequential processing • Sequential Processing Performance ● Take long time to execute task. ● Can’t handle too large task. ● Can’t handle large loads well. ●Return is diminishing. ● More increasingly expensive to make a single processor faster.

17. Performance of parallel processing and sequential processing .2 • Solution: using parallel processing - use lots of relatively fast, cheap processors in parallel.

18. Performance of parallel processing and sequential processing .3 • Parallel Processing Performance ● Cheaper, in terms of price and performance. ● Faster than equivalently expensive uniprocessor machines. ● Scalable. The performance of a particular program may be improved by execution on a large machine.

19. Performance of parallel processing and sequential processing .4 • Parallel Processing Performance ● Reliable. In theory if processors fail we can simply use others. ● Can handle bigger problems. ● Communicate with each other readily, important in calculations.

20. Parallel Processing Evaluation • Several ways to evaluate the parallel processing performance: ● Scale-up ● Speedup ● Efficiency ● Overall solution time ● Price/performance

21. Parallel Processing Evaluation .2 • Scale-up Scale-up is enhanced throughput,refers to the ability of a system ntimes larger to perform an n times larger job, in the same time period as the original system. With added hardware, a formula for scale-up holds the time constant, and measures the increased size of the job which can be done.

22. Sequential System: 100% Task Hardware Time Parallel System: Hardware Hardware 200% Task Time Time Figure 5 Scale-up Parallel Processing Evaluation .3

23. Scale-up = Transaction volume of multiprocessors Transaction volume of uniprocessor Parallel Processing Evaluation .4 • Scale-up measurement formula:

24. Parallel Processing Evaluation .5 • For example, if the uniprocessor system can process 100 transactions in a given amount of time, and the parallel system can process 200 transactions in this amount of time, then the value of scale-up would be equal to 200/100 = 2. • Value 2 indicates the ideal of linear scale-up: when twice as much, hardware can process twice the data volume in the same amount of time.

25. Parallel Processing Evaluation .6 • Speedup Speedup, the improved response time, defined as the time it takes a program to execute in sequential (with one processor) divided by the time it takes to execute in parallel (with many processors). It can be achieved by two ways: breaking up a large task into many small fragments and reducing wait time.

26. Sequential System: 100% Task Hardware Time Parallel System: Hardware Hardware 50% Task Time 50% Task Time Figure 6 Speedup Parallel Processing Evaluation .7

27. Elapsed time of a uniprocessor Speedup = Elapsed time of the multiprocessors Parallel Processing Evaluation .8 • Speedup measurement formula:

28. Parallel Processing Evaluation .9 • For example, if the uniprocessor took 40 seconds to perform a task, and two parallel systems took 20 seconds, then the value of speedup = 40 / 20 = 2. • Value 2 indicates the ideal of linear speedup: when twice as much, hardware can perform the same task in half the time.

29. Parallel Processing Evaluation .10 Table 1 Scale-up and Speedup for Different Types of Workload

30. Parallel Processing Evaluation .11 Figure 7 Linear and actual speedup

31. Parallel Processing Evaluation .12 Amdahl’s Law Amdahl's Law is a law governing the speedup of using parallel processors on a problem, versus using only one sequential processor. Amdahl’s law attempts to give a maximum bound for speedup from the nature of the algorithm:

32. S + P S: purely sequential part P: parallel part S + P = 1 (for simplicity) Maximum speedup = S + P n 1 = Parallel Processing Evaluation .13 Amdahl’s Law

33. Parallel Processing Evaluation .14 Figure 8 Example speedup: Amdahl & Gustafson

34. Parallel Processing Evaluation .15 Gustafson’s Law If the size of a problem is scaled up as the number of processors increases, speedup very close to the ideal speedup is possible. That is, a problem size is virtually never independent of the number of processors.

35. S + (P * n) Maximum speedup = = n + (1 - n) * S S + P Parallel Processing Evaluation .16 Gustafson’s Law

36. Speedup * 100 Efficiency = Number of processors Parallel Processing Evaluation .17 • Efficiency The relative efficiency can be a useful measure as to what percentage of a processor’s time is being spent in useful computation.

37. Parallel Processing Evaluation .18 Figure 9 Optimum efficiency & actual efficiency

38. Parallel Processing Evaluation.19 Figure 10 Optimum number of processors in actual speedup

39. Parallel Processing Evaluation.20 • Problems in Parallel Processing Parallel processing is like a dog’s walking on its hind legs. It is not done well, but you are surprised to find it done at all. ----Steve Fiddes (University of Bristol)

40. Parallel Processing Evaluation.21 • Problems in Parallel Processing ● Its software is heavily platform-dependent and has to be written for a specific machine. ● It also requires a different, more difficult method of programming, since the software needs to appropriately, through algorithms, divide the work across each processor.

41. Parallel Processing Evaluation.22 • Problems in Parallel Processing ● There isn't a wide array of shrink-wrapped software ready for use with parallel machines. ● Parallelization is problem-dependent and cannot be automated. ● Speedup is not guaranteed.

42. Parallel Processing Evaluation.23 • Solution 1: ● Decide which architecture is most appropriate for a given application. The characteristics of application should drive decision as to how it should be parallelized; the form of the parallelization should then determine what kind of underlying system, both hardware and software, is best suited to running your parallelized application.

43. Parallel Processing Evaluation.24 • Solution 2: ● Clustering

44. Major Applications of parallel processing • Clustering ● Clustering is a form of parallel processing that takes a group of workstations connected together in a local-area network and applies middleware to make them act like a parallel machine.

45. Major Applications of parallel processing .2 • Clustering Clustering is a form of parallel processing that takes a group of workstations connected together in a local-area network and applies middleware to make them act like a parallel machine.

46. Major Applications of parallel processing .3 • Clustering ● Parallel processing using Linux Clusters can yield supercomputer performance for some programs that perform complex computations or operate on large data sets. And it can accomplish this task by using cheap hardware. ● Clustering can be used at night when networks are idle, it is an inexpensive alternative to parallel-processing machines.

47. Major Applications of parallel processing .4 • Clustering can work with two separate but similar implementations: ● A Parallel Virtual Machine (PVM), is an environment that allows messages to pass between computers as it would in an actual parallel machine. ● AMessage-Passing Interface (MPI), allows programmers to create message-passing parallel applications, using parallel input/output functions and dynamic process management.

48. Reference • Andrew Boucher, “Parallel Machines” • Stephane vialle, “Past and Future Parallelism Challenges to Encompass sequential Processor evolution”

49. The end Thank you!