MPI Alg Design and Task 0
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@ -23,7 +23,7 @@ on-chip block RAM, or even levering Hybrid Memory Cubes for optimizing parallel
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From a data structure standpoint, the authors of \cite{arifuzzaman_fast_2015} describe the infeasibility of adjacency matrices in large-scale graphs, and this work and other works such as those
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by the authors of \cite{yu_construction_2018} and \cite{liakos_memory-optimized_2016} discuss the appeal of distibuting a graph representation among systems. The author of
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\cite{liakos_memory-optimized_2016} disuss the usage of distributed adjacency lists for assinging vertices to workers. The authors of \cite{liakos_memory-optimized_2016} and
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\cite{liakos_memory-optimized_2016} discuss the usage of distributed adjacency lists for assinging vertices to workers. The authors of \cite{liakos_memory-optimized_2016} and
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\cite{balaji_graph_2016} present other techniques for minimizing communication costs by achieving high compression ratios while maintaining a low compression cost. The Boost Graph Library
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and the Parallel Boost Graph Library both provide appealing features for working with graps, with the latter library notably having interoperability with MPI, Graphviz, and METIS
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\cite{noauthor_overview_nodate}, \cite {noauthor_boost_nodate}.
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@ -15,6 +15,7 @@
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\citation{berry_graph_2007}
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\citation{cook_rage_2018}
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@ -60,7 +60,7 @@ Figure \ref{fig:CC} displays an example graph that leverages color coding to eas
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\label{fig:CC}
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\end{figure}
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\TUsection{Intermediate Database Storage}
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\TUsection{Intermediate Database Storage}\label{sec:db-stor}
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\TUsubsection{Memory Constraint Difficulties}
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Previous works with RAGE have been designed around maximizing performance to limit the longer runtime caused by the state space explosion, such as the works seen by the authors of \cite{cook_rage_2018},
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\cite{li_concurrency_2019}, and \cite{li_combining_2019}. To this end, the output graph is stored in memory during the generation process to minimize disk writing and reading, as well as leverage the
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Chapter5.aux
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\citation{cook_rage_2018}
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\citation{li_concurrency_2019}
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\citation{9150145}
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\citation{7087377}
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15
Chapter5.tex
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Chapter5.tex
@ -46,14 +46,27 @@ attack and compliance graph generation.
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\end{figure}
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\TUsubsection{Algorithm Design}
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The design of the tasking approach is to leverage a pipeline structure with the six tasks and MPI nodes. Each stage of the pipeline will pass the necessary data to the next stage through various MPI messages, where the next stage's nodes will receive the data and execute their tasks. The pipeline is considered fully saturated when each task has a dedicated node. When there are less nodes than tasks, some nodes will processing multiple tasks. When there are more nodes than tasks, additional nodes will be assigned to Tasks 1 and 2. Timings were collected in the serial approach for various networks that displayed more time requirements for Tasks 1 and 2, with larger network sizes requiring vastly more time to be taken in Tasks 1 and 2. As a result, additional nodes are assigned to Tasks 1 and 2.
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The design of the tasking approach is to leverage a pipeline structure with the six tasks and MPI nodes. Each stage of the pipeline will pass the necessary data to the next stage through various MPI messages, where the next stage's nodes will receive the data and execute their tasks. The pipeline is considered fully saturated when each task has a dedicated node. When there are less nodes than tasks, some nodes will processing multiple tasks. When there are more nodes than tasks, additional nodes will be assigned to Tasks 1 and 2. Timings were collected in the serial approach for various networks that displayed more time requirements for Tasks 1 and 2, with larger network sizes requiring vastly more time to be taken in Tasks 1 and 2. As a result, additional nodes are assigned to Tasks 1 and 2. Node allocation can be seen in Figure \ref{fig:node-alloc}.
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For determining which tasks should be handled by the root note, a few considerations were made. Minimizing communication cost and avoiding unnecessary complexity were the main two considerations. In the serial approach, the frontier queue was the primary data structure for the majority of the execution. Rather than using a distributed queue or passing multiple sub-queues between nodes, the minimal option is to pass states individually. This approach also assists in reducing the complexity. Managing multiple frontier queues would require duplication checks, multiple nodes requesting data from and storing data into the database, and devising a strategy to maintain proper queue ordering, all of which would also increase the communication cost. As a result, the root node will be dedicated to Tasks 0 and 3.
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\begin{figure}[htp]
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\includegraphics[width=\linewidth]{"./Chapter5_img/node-alloc.png"}
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\vspace{.2truein} \centerline{}
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\caption{Node Allocation for each Task}
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\label{fig:node-alloc}
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\end{figure}
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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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\TUsubsubsection{Task Four}
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\TUsubsubsection{Task Five}
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\TUsubsubsection{MPI Tags}
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\TUsubsection{Performance Expectations}
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\bibcite{cook_rage_2018}{9}
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\bibcite{cook_scalable_2016}{10}
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\bibcite{CVE-2019-10747}{11}
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\contentsline {figure}{\numberline {4.1}{\ignorespaces A network without Synchronous Firing generating infeasible states}}{18}{}%
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@ -278,53 +278,78 @@ CHAPTER 5.
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\contentsline {section}{\numberline {5.3}\bf Tasking Approach}{26}{}%
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||||
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|
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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}{38}{}%
|
||||
|
||||
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