The objective is to determine the degree of efficiency of humans by placing the human performance curve (execution time as a function of the number of moves) on a discrete scale used to rate the efficiency of algorithms: constant, logarithmic, linear, polynomial, exponential.The numerical execution time of an algorithm is unimportant because it can be improved with faster computers.
Measuring the execution time of our implementation as a function of n and m and performing non-linear least-squares model fitting leads to F = 3.98 · 10-9, α = 2.94 and β = 1.04.
The approach, while being very simple, is able to derive analytic models of execution time as a function of parameters, such as processor speed, network latency, or bandwidth without even looking at the application source.
A detailed description of the kernel configuration has been provided and a theoretical analysis of the GPU execution time as a function of the number of threads managed by the kernels is also reported.
We represented this pattern by means of an algorithmic index, i.e., a curve giving the average execution time as a function of the number of moves (recall that the numerical values of the curve are unimportant, given that it will be used only for correlation analysis).
As a preliminary but illustrative result, we show in Fig. 7 the speedup (the ratio between the execution time on multiple cores and the execution time on single core) of the image classification algorithm in our Spark implementation, as a function of the number of cores within a single node when 1,000 (blue), and 5,000 (orange) images were processed in parallel.
The execution time T as a function of the task is presented in Figure 3 with the algorithmic indexes.
The algorithmic index U N) giving the average execution time T as a function of N increases as the number of nodes+arcs at distance N or lower.
The exponential would therefore remain the best model for measures of quality of fit like the AIC or the Deviance The execution time T as a function of the task is presented in Figure 4.
The diagram of Fig. 5 shows the execution time (in seconds) as a function of the number <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0018961.e256.PNG" class= inline-graphic"/> of vertices of the graphs.
