1 / 7

Amdahl's law

Amdahl's law. Example from Wikipedia. Suppose a task is split into four consecutive parts: P1 ( takes 11% of the time required), P2(18%), P3(23%) and P4(48%).

sfauver
Download Presentation

Amdahl's law

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Amdahl's law

  2. Example from Wikipedia • Suppose a task is split into four consecutive parts: P1 ( takes 11% of the time required), P2(18%), P3(23%) and P4(48%). • Further suppose then that part P1 is not sped up, while P2 is sped up by a factor of 5 times, P3 is sped up 20×, and P4 is sped up 1.6×.

  3. Improvement in speed • We will work in a time unit where the original task takes 1 unit. That time is divided up into the four subtasks 0.11 + 0.18 + 0.23 + 0.48 = 1 • Now the improved version: the 11% still takes the same amount of time, while the 18% is divided by 5 (i.e. is 5 times faster), etc.

  4. Speed • The original speed was one task per one unit of time. • If the original time was 1, the new time is 0.4575 – a little more than twice as fast. Speed is now “one task” / “new time” :

  5. Amdahl’s law • In Amdahl’s Law we break the process into two pieces – a parallelizable part P and a non-parallelizable part (1-P). • If we assume that there are N processors (or pipelines or whatever) then the improvement of the parallelizable part is N.

  6. Limit as N  ∞   1/(1-P) If a process is 90% parallelizable, then the maximal improvement in speed is 1/(1-.9) or 10. It can be made 10 times faster. “For this reason, parallel computing is only useful for either small numbers of processors, or problems with very high values of P: so-called embarrassingly parallel problems. A great part of the craft of parallel programming consists of attempting to reduce the component (1 – P) to the smallest possible value.”

  7. Reference • http://en.wikipedia.org/wiki/Amdahl's_law

More Related