MPI Tasks 1, 2, 4, 5
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Chapter5.tex
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\TUsubsubsection{Communication Structure}
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\TUsubsubsection{Task Zero}
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Task Zero is performed by the root node, and is a conditional task; it is not guaranteed to be executed at each pipeline iteration. Task Zero is only executed when the frontier is empty, but the database still holds unexplored states. This occurs when there are memory constraints, and database storage is performed during execution to offload the demand, as discussed in Section \ref{sec:db-stor}. After the completion of Task 0, the frontier has a state popped, and the root node sends the state to n$_1$. If the frontier is empty, the root node sends the finalize signal to all nodes.
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\TUsubsubsection{Task One}
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\TUsubsubsection{Task Two}
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\TUsubsubsection{Task Three}
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Task Three is performed only by the root node, and no division of work is necessary. The work performed during this task is that seen in Figure \ref{fig:sync-fire}.
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\TUsubsubsection{Task Four and Task Five}
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Intermediate database operations, though not frequent and may never occur for small graphs, are lengthy and time-consuming when they do occur. As discussed in Section \ref{sec:db-stor}, the two main memory consumers are the frontier and the instance, both of which are contained by the root node. Since the database storage requests are blocking, the pipeline would halt for a lengthy period of time while waiting for the root node to finish potentially two large storages. Tasks 4 and 5 work to alleviate the stall by executing independently of the regular pipeline execution flow since no other task relies on data sent from these tasks. The root node can then asynchronously send the frontier and instance to the appropriate nodes as needed, clear its memory, and continue execution without delay.
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\TUsubsubsection{Task 0}
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Task 0 is performed by the root node, and is a conditional task; it is not guaranteed to be executed at each pipeline iteration. Task 0 is only executed when the frontier is empty, but the database still holds unexplored states. This occurs when there are memory constraints, and database storage is performed during execution to offload the demand, as discussed in Section \ref{sec:db-stor}. After the completion of Task 0, the frontier has a state popped, and the root node sends the state to n$_1$. If the frontier is empty, the root node sends the finalize signal to all nodes.
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\TUsubsubsection{Task 1}
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Task 1 begins by distributing the workload between nodes based on the local task communicator rank. Rather than splitting the exploit list at the root node and sending sub-lists to each node allocated to Task 1, each node checks its local communicator rank and performs a modulo operation with the number of nodes allocated to determine whether it should proceed with the current iteration of the exploit loop. Since the exploit list is static, each node has the exploit list initialized prior to the generation process, and communication cost can be avoided from sending sub-lists to each node. Each node in Task 1 works to compile a reduced exploit list that is applicable to the current network state. A breakdown of the Task 1 distribution can be seen in Figure \ref{fig:Task1-Data-Dist}.
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\begin{figure}[htp]
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\includegraphics[width=\linewidth]{"./Chapter5_img/Task1-Data-Dist.png"}
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\vspace{.2truein} \centerline{}
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\caption{Data Distribution of Task One}
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\label{fig:Task1-Data-Dist}
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\end{figure}
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Once the computation work of Task 1 is completed, each node must send their compiled applicable exploit list to Task 2. Rather than merging all lists and splitting them back out in Task 2, each node in Task 1 will send an applicable exploit list to at most one node allocated to Task 2. Based on the allocation of nodes seen in Figure \ref{fig:node-alloc}, there are 2 potential cases: the number of nodes allocated to Task 1 is equal to the number of nodes allocated to Task 2, or the number of nodes allocated to Task 1 is one greater than the number of nodes allocated to Task 2. For the first case, each node in Task 1 sends the applicable exploit list to its global rank+n$_1$). This case can be seen in Figure \ref{fig:Task1-Case1}. For the second case, since there are more nodes allocated to Task 1 than Task 2, node n$_1$ scatters its partial applicable exploit list in the local Task 1 communicator, and all other Task 1 nodes follow the same pattern seen in the first case. This second case can be seen in Figure \ref{fig:Task1-Case2}.
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\begin{figure}[htp]
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\includegraphics[width=\linewidth]{"./Chapter5_img/Task1-Case1.png"}
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\vspace{.2truein} \centerline{}
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\caption{Communication From Task 1 to Task 2 when the Number of Nodes Allocated is Equal}
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\label{fig:Task1-Case1}
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\end{figure}
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\begin{figure}[htp]
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\includegraphics[width=\linewidth]{"./Chapter5_img/Task1-Case2.png"}
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\vspace{.2truein} \centerline{}
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\caption{Communication From Task 1 to Task 2 when Task 1 Has More Nodes Allocated}
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\label{fig:Task1-Case2}
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\end{figure}
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\TUsubsubsection{Task 2}
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\TUsubsubsection{Task 3}
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Task 3 is performed only by the root node, and no division of work is necessary. The work performed during this task is that seen from the Synchronous Firing figure (Figure \ref{fig:sync-fire}). The root node will continuously check for new states until the Task 2 finalize signal is detected. When the root node has processed all states and has received the Task 2 finalize signal, it will complete Task 3 by sending the instance and/or frontier to Task 4 and/or 5, respectively if applicable, then proceeds to Task 0.
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\TUsubsubsection{Task 4 and Task 5}
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Intermediate database operations, though not frequent and may never occur for small graphs, are lengthy and time-consuming when they do occur. As discussed in Section \ref{sec:db-stor}, the two main memory consumers are the frontier and the instance, both of which are contained by the root node. Since the database storage requests are blocking, the pipeline would halt for a lengthy period of time while waiting for the root node to finish potentially two large storages. Tasks 4 and 5 work to alleviate the stall by executing independently of the regular pipeline execution flow. Since Tasks 4 and 5 do not send any data, no other tasks must wait for these tasks to complete. The root node can then asynchronously send the frontier and instance to the appropriate nodes as needed, clear its memory, and continue execution without delay.
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\TUsubsubsection{MPI Tags}
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\TUsubsection{Performance Expectations}
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\bibcite{CVE-2019-10747}{11}
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\contentsline {subsection}{\numberline {5.3.1}\it Introduction to the Tasking Approach}{26}{}%
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{\hfill \ }
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{\hfill \ }
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|
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|
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|
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\contentsline {subsubsection}{A Sub-subsection in an Appendix}{40}{}%
|
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|
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