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[[File:OPTIMIZATIONCOMPARISON.png]]
= CONCLUSIONS =
== OVERALL TIME COMPARISONS ==
Below are the final comparisons of all execution times between the CPU and GPU.
All times are in milliseconds.
[[File:finalCompare.png]]
== APPLICATION OUTPUT ==
Upon completion of the application it will create a file based on the output of the algorithm. The following image below displays that output comparing the original program to the parallelized program.
[[File:outputs.png]]
== FINAL THOUGHTS ==
Upon completion of this Project I have learned a few things:
First, I learned that not all program can be parallelized even if they seem to be a good candidate to begin with.
Secondly, understand the algorithm of the application is a key factor in being able to optimize the solution, because sometimes you will need to rearrange the code in order to obtain better performance from the GPU and understanding the algorithm will help ensure that the output at the end of the program will remain the same.
Thirdly the management of resources and constraints, having registers, shared memory, constant memory, latency, threads, and multi-processors are all factors which need to be considered when using the GPU. Understanding how these resources can impact and influence your program helps deciding which ones to use in specific situations.