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→Potential Speed Increase
4.49 107.11 4.88 1406087506 0.00 0.00 std::vector<double, std::allocator<double> >::operator[](unsigned int)''
== Potential Speed Increase with Amdahls Law ==
Using Amdahls Law ---- > Sn = 1 / ( 1 - P + P/n )
We can examine how fast out program is capable of increasing its speed.
P = 80is the part of the program we want to optimize which from above is 89.17%; n = 480 the amount of processors we will use. One GPU card has 384 processors or CUDA cores and another GPU we will use has 1020 processor or CUDA cores. Applying the algorithm gives us. Amdahls Law for GPU with 384 Cores---- > Sn = 1 / ( 1 - 0.8919 + 0.8919/384 ) Sn = 9.0561125222S480 Amdahls Law for GPU with 1024 Cores---- > Sn = 1 / ( 1 - 0.80 8919 + 0.80 8919/ 480 1024 ) Sn = 49.176753777 Therefor According to Amdahls law we can expect a 9x increase in speed.96 97 seconds to execute main / 9 amdahls law = 10.7777 seconds to execute after using GPUInterestingly according to the law the difference in GPU cores does not significantly increase speed. Future tests will confirm or deny these results. --------------------------------------------------------------------------------------------------== Potential Speed Increase with Gustafsons Law ==
Gustafsons Law S(n) = n - ( 1 - P ) ∙ ( n - 1 )