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Measuring Performance

Measuring Performance. How should the performance of a parallel computation be measured? Traditional measures like MIPS and MFLOPS really don’t cut it New ways to measure parallel performance are needed Speedup Efficiency. Speedup.

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Measuring Performance

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  1. Measuring Performance • How should the performance of a parallel computation be measured? • Traditional measures like MIPS and MFLOPS really don’t cut it • New ways to measure parallel performance are needed • Speedup • Efficiency ICSS531 - Speedup

  2. Speedup • Speedup is the most often used measure of parallel performance • If • Ts is the best possible serial time • Tn is the time taken by a parallel algorithm on n processors • Then ICSS531 - Speedup

  3. Read Between the Lines • Exactly what is meant by Ts (i.e. the time taken to run the fastest serial algorithm on one processor) • One processor of the parallel computer? • The fastest serial machine available? • A parallel algorithm run on a single processor? • Is the serial algorithm the best one? • To keep things fair, Ts should be the best possible time in the serial world ICSS531 - Speedup

  4. Speedup’ • A slightly different definition of speedup also exists. • The time taken by the parallel algorithm on one processor divided by the time taken by the parallel algorithm on N processors. • However this is misleading since many parallel algorithms contain extra operations to accommodate the parallelism (e.g the communication) • The result is Ts is increased thus exaggerating the speedup. ICSS531 - Speedup

  5. Factors That Limit Speedup • Software Overhead • Even with a completely equivalent algorithm, software overhead arises in the concurrent implementation • Load Balancing • Speedup is generally limited by the speed of the slowest node. So an important consideration is to ensure that each node performs the same amount of work • Communication Overhead • Assuming that communication and calculation cannot be overlapped, then any time spent communicating the data between processors directly degrades the speedup ICSS531 - Speedup

  6. Linear Speedup • Which ever definition is used the ideal is to produce linear speedup • A speedup of N using N processors • However in practice the speedup is reduced from its ideal value of N • Superlinear speedup results when • unfair values are used for Ts • Differences in the nature of the hardware used ICSS531 - Speedup

  7. Speedup Curves Superlinear Speedup Linear Speedup Speedup Typical Speedup Number of Processors ICSS531 - Speedup

  8. Efficiency • Speed up does not measure how efficiently the processors are being used • Is it worth using 100 processors to get a speedup of 2? • Efficiency is defined as the ratio of the speedup and the number of processors required to achieve it • The efficiency is bounded from above by 1 ICSS531 - Speedup

  9. Example ICSS531 - Speedup

  10. Speedup Curve ICSS531 - Speedup

  11. Amdahl’s Law • A parallel computation has two types of operations • Those which must be executed in serial • Those which can be executed in parallel • Amdahl’s law states that the speedup of a parallel algorithm is effectively limited by the number of operations which must be performed sequentially ICSS531 - Speedup

  12. Amdahl’s Law • Let the time taken to do the serial calculations be some fraction σ of the total time ( 0 < σ  1) • The parallelizable portion is 1- σ of the total • Assuming linear speedup • Tserial = σT1 • Tparallel = (1- σ)T1/N • By substitution ICSS531 - Speedup

  13. Consequences of Amdahl’s • Say we have a program containing 100 operations each of which take 1 time unit. • Suppose σ=.2, using 80 processors • Speedup = 100 / (20 + 80/80) = 100 / 21 < 5 • A speedup of only 5 is possible no matter how many processors are available • So why bother with parallel computing? Just wait for a faster processor ICSS531 - Speedup

  14. Avoiding Amdahl • There are several ways to avoid Amdahl’s law • Concentrate on parallel algorithms with small serial components • Amdahl’s law is not complete in that it does not take into account problem size ICSS531 - Speedup

  15. Classifying Parallel Programs • Parallel programs can be placed into broad categories based on expected speedups • Trivial Parallel • Assumes complete parallelism with no overhead due to communication • Divide and Conquer • N log N speedup • Communication Bound Parallelism ICSS531 - Speedup

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