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\citation{Amdahl}
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Schrick-Noah_MPI-Tasking.tex
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@ -304,15 +304,7 @@ Each parameter discussed in this section was individually changed until all perm
\section{Analysis and Results}
Due to a limited amount of compute time, only preliminary results were gathered, instead of deploying the entire testing benchmark suite. The preliminary results gathered were intended to be the ``slowest" tests that would have the lowest speedup. The ``worst-case", minimum-bound data was collected to determine potential success of this approach. If the slowest tests still yielded promising speedups or efficiencies, then this approach would be viable and appealing for future, in-depth testing that could stress each component of the generation process.
Exploratory data analysis was performed on the resulting data using Python to ascertain data relationships. Due to the multivariate nature of the data, there is difficulty visualizing all four independent variables (parameters) and the outcome (runtime) simultaneously. Figure \ref{fig:para_coords} makes uses of Plotly's parallel coordinates to visualize the parameters and their relationship to the runtime. Using pivot tables, Figures \ref{fig:nodes-exp} and \ref{fig:appl-load} show the runtime as they relate to the average of each individual parameter. These figures display the expected outcome: as the number of nodes increase, the runtime decreases, and as the number of exploits, applicability of exploits, and database load increases, the runtime likewise increases.
\begin{figure}[htp]
\centering
\includegraphics[width=\linewidth]{"./images/para_coords.png"}
\vspace{.2truein} \centerline{}
\caption{Parallel Coordinates Plot of MPI Tasking Parameters and Runtime(ms)}
\label{fig:para_coords}
\end{figure}
Exploratory data analysis was performed on the resulting data using Python to ascertain data relationships. Due to the multivariate nature of the data, there is difficulty visualizing all four independent variables (parameters) and the outcome (runtime) simultaneously. Using pivot tables, Figures \ref{fig:nodes-exp} and \ref{fig:appl-load} show the runtime as they relate to the average of each individual parameter. These figures display the expected outcome: as the number of nodes increase, the runtime decreases, and as the number of exploits, applicability of exploits, and database load increases, the runtime likewise increases.
\begin{figure}
\centering
@ -352,7 +344,7 @@ Figure \ref{fig:overall-efficiency} displays the overall minimum, maximum, and m
\label{fig:overall-efficiency}
\end{figure}
Speedups and efficiencies were also computed across each parameter. Using pivot tables, mean speedups and mean efficiencies were computed for a parameter across all node configurations. Figures \ref{fig:param-exploit}, \ref{fig:param-appl}, \ref{fig:param-load} display the speedups and efficiencies of the exploit parameter, applicability of exploits parameter, and the database load parameter, respectively. The number of nodes has the largest impact on the exploit parameter, and Figure \ref{fig:param-exploit} illustrates that even when fewer nodes are used, speedup can still be obtained as the exploit list grows in size. Figure \ref{fig:param-appl} demonstrates that though Task 2 has less of an impact on overall runtime and contribution to speedup, speedup is still achievable as more compute nodes are added and as the applicability of exploits increase. Though database load was not a parameter to easily include in preliminary testing, speedup is expected as this parameter changes. By dedicating nodes to solely handle database operations, the tasking pipeline is able to move to new state generation without the need to wait for all preceding database operations to complete.
Speedups and efficiencies were also computed across each parameter. Using pivot tables, mean speedups and mean efficiencies were computed for a parameter across all node configurations. Figures \ref{fig:param-exploit} and \ref{fig:param-appl} display the speedups and efficiencies of the exploit parameter and the applicability of exploits parameter, respectively. The number of nodes has the largest impact on the exploit parameter, and Figure \ref{fig:param-exploit} illustrates that even when fewer nodes are used, speedup can still be obtained as the exploit list grows in size. Figure \ref{fig:param-appl} demonstrates that though Task 2 has less of an impact on overall runtime and contribution to speedup, speedup is still achievable as more compute nodes are added and as the applicability of exploits increase. Though database load was not a parameter to easily include in preliminary testing, speedup is expected as this parameter changes. By dedicating nodes to solely handle database operations, the tasking pipeline is able to move to new state generation without the need to wait for all preceding database operations to complete.
\begin{figure}
\centering