In the above, f  represents the fraction of the code that cannot be parallelized. The remaining fraction, 1 – f, is parallelizable. Optimally, if the parallelized code scales linearly with the number of workers once parallelized, then the runtime reduces to (1-f)/N  and hence

TN = [f + (1-f)/N]*T1

Speedup ratio, S, and parallel efficiency, E, may be used:

  1. to provide an estimate for how well a code sped up if it was parallelized. For example, if f = 0.1 the speedup bound above predicts a 10 fold speedup in the limit. On the other hand, a code that is 50% parallelizable will at best see a factor of 2 speedup. In the latter example, a potentional speedup of only a facotr of two may not be compelling to initiate a code parallelization effort — especially if it takes a fair amount of effort to parallelize the code.
  2. to generate a plot of time vs. workers to understand the behavior of the parallelized code.
  3. to see how the parallel efficiency tends toward the point of diminishing returns. With this information, you would be able to determine, for a fixed problem size, what is the optimal number of workers to use.

    

              
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