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In this project, the goal was to use the Julia programming language andparallelization to write a fast map reduce algorithm to count word frequenciesacross large numbers of documents. We first implement the word frequencycounter algorithm on a CPU using two processes with MPI. Then, we createanother implementation, but on a GPU using the Julia CUDA library, though notusing the in built map reduce algorithm within FoldsCUDA.jl. After doing this,we apply our CPU and GPU algorithms to count the frequencies of words inspeeches given by Presidents George W Bush, Barack H Obama, Donald J Trump, andJoseph R Biden with the aim of finding patterns in word choice that could beused to uniquely identify each President. We find that each President does havecertain words that they use distinctly more often than their fellow Presidents,and these words are not surprising given the political climate at the time.  The goal of this project was to create faster MapReduce algorithms in Juliaon the CPU and GPU than the ones that have already been written previously. Wepresent some simple cases of mapping functions where our GPU algorithmoutperforms Julia's FoldsCUDA implementation. We also discuss ideas for furtheroptimizations in the case of counting word frequencies in documents and forthese specific mapping functions.

MapReduce for Counting Word Frequencies with MPI and GPUs