Writeup and plots for speedup and efficiency per param

Data/para_data.csv

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Data/timing.csv

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Schrick-Noah_MPI-Tasking.tex

@@ -246,7 +246,7 @@ Task 1 loops through the number of exploits and checks each exploit against the
 \end{figure}
 
 \subsection{Applicability of Exploits}
-When the number of exploits is artificially increased, the runtime for the overall generation process also increases. However, solely increasing the number of exploits adds a strain on only Task 1; Tasks 0, 2, 3, 4, and 5 are not adequately stress-tested through the number of exploits alone. As a result, additional parameters will need to be altered to capture a thorough image of the tasking performance. 
+When the number of exploits is artificially increased, the runtime for the overall generation process also increases. However, solely increasing the number of exploits adds a strain on only Task 1; Tasks 0, 2, 3, 4, and 5 are not adequately stress tested through the number of exploits alone. As a result, additional parameters will need to be altered to capture a thorough image of the tasking performance. 
 
 One parameter that can be carefully altered without affecting the resulting graph is the applicability of exploits. As the number of exploits applicable to any state grows, the runtime for Task 2 similarly increases since it must process all applicable exploits and generate new states and edges from the current state. In order for an exploit to be applicable and to not change the resulting graph, the exploit needs to have a precondition that is universally true, with a postcondition that has no effect. For the automobile example, an alteration to the ``not applicable" exploit seen in Figure \ref{fig:NA-exp} can be performed. The new, artificially applicable exploit can be seen in Figure \ref{fig:Appl-exp}. These artificial exploits will be applicable for any asset at any state in the test network, since no car in this example will ever posses a quality that allows it to fly. Likewise, though the exploit will be processed, the postcondition updates the car quality to match the quality it already contains (``flying$\_$car=false" is instantiated in the input network model). The update keyword in the postcondition still triggers the update function, even if no change is actually made. By updating the car quality in this manner, it is ensured that no change to the resulting graph is made, while still gathering accurate timing data and not skipping any functions called in Task 2. 
 
@@ -285,7 +285,7 @@ The database load parameter was changed based on percentage of the total resulti
     \item{100\% Load (Write to the database on every new state) - DBLoad = 1}
 \end{itemize}
 
-The database load parameter stresses Tasks 0, 4, and 5. Task 4 will be stressed on all load parameters, except for when the load is 0\% (size 395), which serves as the control. Task 4 will experience the greatest stress when the load parameter is 100\% (size 1), since as soon as new states are discovered in previous tasks, Task 4 will begin. Task 0 and Task 5 will experience stress at the same intervals. When the queue of unexplored states increases to a size greater than the load parameter, Task 5 will empty the queue, and Task 0 will be forced to pull new states from the database.
+The database load parameter stresses Tasks 0, 4, and 5. Task 4 will be stress tested on all load parameters, except for when the load is 0\% (size 395), which serves as the control. Task 4 will experience the greatest workload when the load parameter is 100\% (size 1), since as soon as new states are discovered in previous tasks, Task 4 will begin. Task 0 and Task 5 will experience stress at the same intervals. When the queue of unexplored states increases to a size greater than the load parameter, Task 5 will empty the queue, and Task 0 will be forced to pull new states from the database.
 
 \subsection{Testing Platform} \label{sec:test-platform}
 All data was collected on a 13 node cluster, with 12 nodes serving as dedicated compute nodes, and 1 node serving as the login node. Each compute node has a configuration as follows:
@@ -340,7 +340,7 @@ Figure \ref{fig:overall-speedup} displays the overall minimum, maximum, and mean
         \label{fig:overall-speedup}
 \end{figure}
 
-Figure \ref{fig:overall-efficiency} displays the overall minimum, maximum, and mean of efficiency across all problem sizes. In terms of efficiency, 2 compute nodes offer the greatest value. While the 2 compute node configuration does offer the greatest efficiency, it does not provide a speedup greater than 1.0 on any of the testing cases conducted. The results also demonstrate that an odd number of compute nodes in a fully saturated pipeline has better efficiency that an even number of compute nodes. When referring to Figure \ref{fig:node-alloc}, when there is an odd number number of compute nodes, Task 1 is allocated more nodes than Task 2. Task 1 was responsible for iterating through an increased size of the exploit list, so more nodes is advantageous in distributing the workload. However, when many exploits were not applicable, Task 2 had a lower workload. Some test cases only had 6 applicable exploits, which is a substantially lower workload for Task 2 compared to cases where Task 1 had upwards of 49,000 exploits. As the applicability of exploits increase, the disparity in efficiency for odd and even number of nodes is not present.
+Figure \ref{fig:overall-efficiency} displays the overall minimum, maximum, and mean of efficiency across all problem sizes. In terms of efficiency, 2 compute nodes offer the greatest value. While the 2 compute node configuration does offer the greatest efficiency, it does not provide a speedup greater than 1.0 on any of the testing cases conducted. The results also demonstrate that an odd number of compute nodes in a fully saturated pipeline has better efficiency that an even number of compute nodes. When referring to Figure \ref{fig:node-alloc}, when there is an odd number number of compute nodes, Task 1 is allocated more nodes than Task 2. Task 1 was responsible for iterating through an increased size of the exploit list, so more nodes is advantageous in distributing the workload. However, when many exploits were not applicable, Task 2 had a lower workload. Some test cases only had 6 applicable exploits, which is a substantially lower workload for Task 2 compared to cases where Task 1 had upwards of 49,000 exploits. As the applicability of exploits increases, the disparity in efficiency for odd and even number of nodes is not present.
 
 \begin{figure}[htp]
     \centering
@@ -350,44 +350,30 @@ Figure \ref{fig:overall-efficiency} displays the overall minimum, maximum, and m
         \label{fig:overall-efficiency}
 \end{figure}
 
-\section{Results} \label{sec:Tasking-Results}
-A series of tests were conducted on the platform described at the beginning of Section \ref{sec:test-platform}, and results were collected in regards to the effect of the MPI Tasking approach on increasing sizes of exploit lists for a varying number of nodes. The exploit list initially began with 6 items, and each test scaled the number of exploits by a factor of 2. The final test was with an exploit list with 49,512 entries. If all of the items in these exploit lists were applicable, the runtime would be too great for feasible testing due to the state space explosion. To prevent state-space explosion but still gather valid results, each exploit list in the tests contained 6 exploits that could be applicable, and all remaining exploits were not applicable. The not applicable exploits were created in a fashion similar to that seen in Figure \ref{fig:NA-exp}. By creating a multitude of not applicable exploits, testing can safely be conducted by ensuring state space explosion would not occur while still observing the effectiveness of the tasking approach.
-
-The results of the Tasking Approach can be seen in Figure \ref{fig:Spd-Eff-Task}. In terms of speedup, when the number of entries in the exploit list is small, the serial approach has better performance. This is expected due to the communication cost requiring more time than it does to generate a state, as discussed in Section \ref{sec:Task-perf-expec}. However, as the number of items in the exploit list increase, the Tasking Approach quickly begins to outperform the serial approach. It is notable that even when the tasking pipeline is not fully saturated (when there are less compute nodes assigned than tasks), the performance is still approximately equal to that of the serial approach. The other noticeable feature is that as more compute nodes are assigned, the speedup continues to increase.
-
-In terms of efficiency, 2 compute nodes offer the greatest value since the speedup using 2 compute nodes is approximately 1.0 as the exploit list size increases. While the 2 compute node option does offer the greatest efficiency, it does not provide a speedup greater than 1.0 on any of the testing cases conducted. The results also demonstrate that an odd number of compute nodes in a fully saturated pipeline has better efficiency that an even number of compute nodes. When referring to Figure \ref{fig:node-alloc}, when there is an odd number number of compute nodes, Task 1 is allocated more nodes than Task 2. In the testing conducted, Task 1 was responsible for iterating through an increased size of the exploit list, so more nodes is advantageous in distributing the workload. However, since many exploits were not applicable, Task 2 had a lower workload where only 6 exploits could be applicable. This will be further elaborated upon in Section \ref{sec:FW}, but it is expected that efficiency will increase for real networks, since nodes in Task 2 will see a realistic workload.
-
-Figures \ref{fig:Tasking-RT}, \ref{fig:Tasking-Spd}, and \ref{fig:Tasking-Eff} display the results of the tasking approach for runtime in milliseconds, speedup, and efficiency respectively in table format.
-
-
+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. Figure \ref{fig:param-load} highlights the increasing runtime as more database operations are performed. 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
-    \includegraphics[width=\linewidth]{"./images/Speedup-Esize-Tasking.png"}
-    \includegraphics[width=\linewidth]{"./images/Eff-Esize-Tasking.png"}
-    \caption{Speedup and Efficiency of the MPI Tasking Approach for a Varying Number of Compute Nodes with an Increasing Problem Size}
-    \label{fig:Spd-Eff-Task}
+    \includegraphics[width=\linewidth]{"./images/exploit-speedup.png"}
+    \includegraphics[width=\linewidth]{"./images/exploit-eff.png"}
+    \caption{Mean Speedup and Efficiency for the Exploit Parameter Across the Number of Compute Nodes}
+    \label{fig:param-exploit}
 \end{figure}
 
 \begin{figure}
     \centering
-    \includegraphics[width=\linewidth]{"./images/Tasking_RT.png"}
-    \caption[MPI Tasking Approach Runtime Results]{Results for the MPI Tasking Approach in Terms of Runtime in Milliseconds}
-    \label{fig:Tasking-RT}
+    \includegraphics[width=\linewidth]{"./images/appl-speedup.png"}
+    \includegraphics[width=\linewidth]{"./images/appl-eff.png"}
+    \caption{Mean Speedup and Efficiency for the Applicability of Exploit Parameter Across the Number of Compute Nodes}
+    \label{fig:param-appl}
 \end{figure}
 
 \begin{figure}
     \centering
-    \includegraphics[width=\linewidth]{"./images/Tasking_Spd.png"}
-    \caption{Results for the MPI Tasking Approach in Terms of Speedup}
-    \label{fig:Tasking-Spd}
-\end{figure}
-
-\begin{figure}
-    \centering
-    \includegraphics[width=\linewidth]{"./images/Tasking_Eff.png"}
-    \caption{Results for the MPI Tasking Approach in Terms of Efficiency}
-    \label{fig:Tasking-Eff}
+    \includegraphics[width=\linewidth]{"./images/load-speedup.png"}
+    \includegraphics[width=\linewidth]{"./images/load-eff.png"}
+    \caption{Mean Speedup and Efficiency for the Database Load Parameter Across the Number of Compute Nodes}
+    \label{fig:param-load}
 \end{figure}