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Noah L. Schrick 2023-04-22 17:41:17 -05:00
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Bibliography.bib
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Schrick-Noah_MPI-Tasking.aux
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@ -41,7 +41,7 @@
\citation{liakos_memory-optimized_2016} \citation{liakos_memory-optimized_2016}
\citation{liakos_memory-optimized_2016} \citation{liakos_memory-optimized_2016}
\citation{balaji_graph_2016} \citation{balaji_graph_2016}
\citation{noauthor_overview_nodate} \citation{noauthor_parmetis_nodate}
\citation{noauthor_boost_nodate} \citation{noauthor_boost_nodate}
\citation{ou_scalable_2006} \citation{ou_scalable_2006}
\citation{cook_scalable_2016} \citation{cook_scalable_2016}
@ -141,7 +141,7 @@
\bibcite{yu_construction_2018}{17} \bibcite{yu_construction_2018}{17}
\bibcite{liakos_memory-optimized_2016}{18} \bibcite{liakos_memory-optimized_2016}{18}
\bibcite{balaji_graph_2016}{19} \bibcite{balaji_graph_2016}{19}
\bibcite{noauthor_overview_nodate}{20} \bibcite{noauthor_parmetis_nodate}{20}
\bibcite{noauthor_boost_nodate}{21} \bibcite{noauthor_boost_nodate}{21}
\bibcite{cook_scalable_2016}{22} \bibcite{cook_scalable_2016}{22}
\bibcite{li_concurrency_2019}{23} \bibcite{li_concurrency_2019}{23}

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Schrick-Noah_MPI-Tasking.bbl
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@ -103,14 +103,16 @@ J.~Balaji and R.~Sunderraman, ``Graph {Topology} {Abstraction} for
{ACM} {Workshop} on {High} {Performance} {Graph} {Processing}}, pp.~27--34, {ACM} {Workshop} on {High} {Performance} {Graph} {Processing}}, pp.~27--34,
May 2016. May 2016.
\bibitem{noauthor_overview_nodate} \bibitem{noauthor_parmetis_nodate}
``An {Overview} of the {Parallel} {Boost} {Graph} {Library} - 1.75.0,'' 2009. K.~Lab, ``Parmetis - parallel graph partitioning and fill-reducing matrix
ordering.''
\newblock http://glaros.dtc.umn.edu/gkhome/metis/parmetis/overview.
\bibitem{noauthor_boost_nodate} \bibitem{noauthor_boost_nodate}
J.~Siek, L.-Q. Lee, and A.~Lumsdaine, ``The {Boost} {Graph} {Library}, vers. J.~Siek, L.-Q. Lee, and A.~Lumsdaine, ``The boost graph library, vers.
1.75.0.'' 1.75.0.''
\newblock \newblock
{https://www.boost.org/doc/libs/1$\_$75$\_$0/libs/graph/doc/index.html}. https://www.boost.org/doc/libs/1$\_$75$\_$0/libs/graph/doc/index.html.
\bibitem{cook_scalable_2016} \bibitem{cook_scalable_2016}
K.~Cook, T.~Shaw, J.~Hale, and P.~Hawrylak, ``Scalable attack graph K.~Cook, T.~Shaw, J.~Hale, and P.~Hawrylak, ``Scalable attack graph
@ -139,6 +141,8 @@ M.~Li, P.~Hawrylak, and J.~Hale, ``Combining {OpenCL} and {MPI} to support
\bibitem{Slurm} \bibitem{Slurm}
SchedMD, ``Slurm {Workload} {Manager}.'' SchedMD, ``Slurm {Workload} {Manager}.''
https://slurm.schedmd.com/overview.html, Apr. 2023.
\newblock Version 23.02.
\bibitem{Amdahl} \bibitem{Amdahl}
G.~M. Amdahl, ``Validity of the single processor approach to achieving large G.~M. Amdahl, ``Validity of the single processor approach to achieving large

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Schrick-Noah_MPI-Tasking.blg
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Database file #1: Bibliography.bib Database file #1: Bibliography.bib
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This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Arch Linux) (preloaded format=pdflatex 2023.4.3) 22 APR 2023 17:12 This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Arch Linux) (preloaded format=pdflatex 2023.4.3) 22 APR 2023 17:39
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@ -709,16 +709,21 @@ Underfull \hbox (badness 1859) in paragraph at lines 65--68
[] []
Underfull \hbox (badness 10000) in paragraph at lines 110--114 Underfull \hbox (badness 10000) in paragraph at lines 112--116
[]\OT1/ptm/m/n/8 J. Siek, L.-Q. Lee, and A. Lumsdaine, []\OT1/ptm/m/n/8 J. Siek, L.-Q. Lee, and A. Lumsdaine,
[] []
Underfull \hbox (badness 10000) in paragraph at lines 110--114 Underfull \hbox (badness 10000) in paragraph at lines 112--116
\OT1/ptm/m/n/8 ``The Boost Graph Library, vers. 1.75.0.'' \OT1/ptm/m/n/8 ``The boost graph library, vers. 1.75.0.''
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[11 <./images/load-speedup.png> <./images/load-eff.png>]) [11 <./images/load-speedup.png> <./images/load-eff.png>]
Underfull \hbox (badness 10000) in paragraph at lines 143--146
[]\OT1/ptm/m/n/8 SchedMD, ``Slurm Workload Manager.''
[]
)
** Conference Paper ** ** Conference Paper **
Before submitting the final camera ready copy, remember to: Before submitting the final camera ready copy, remember to:
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Schrick-Noah_MPI-Tasking.tex
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@ -67,7 +67,7 @@ Other works, such as those by the authors of \cite{zhang_boosting_2017} and \cit
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 by the authors of \cite{yu_construction_2018} and \cite{liakos_memory-optimized_2016} discuss the appeal of distributing a graph representation across systems. 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 by the authors of \cite{yu_construction_2018} and \cite{liakos_memory-optimized_2016} discuss the appeal of distributing a graph representation across systems.
The author of \cite{liakos_memory-optimized_2016} discusses the usage of distributed adjacency lists for assigning vertices to workers. The author of \cite{liakos_memory-optimized_2016} discusses the usage of distributed adjacency lists for assigning vertices to workers.
The authors of \cite{liakos_memory-optimized_2016} and \cite{balaji_graph_2016} present other techniques for minimizing communication costs by achieving high compression ratios while maintaining a low compression cost. The authors of \cite{liakos_memory-optimized_2016} and \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 and the Parallel Boost Graph Library both provide appealing features for working with graphs, with the latter library notably having interoperability with MPI, Graphviz, and METIS \cite{noauthor_overview_nodate}, \cite{noauthor_boost_nodate}. The Boost Graph Library and the Parallel Boost Graph Library both provide appealing features for working with graphs, with the latter library notably having interoperability with MPI, Graphviz, and METIS \cite{noauthor_parmetis_nodate}, \cite{noauthor_boost_nodate}.
There have also been numerous approaches at generation improvement specific to attack graphs. As a means of improving scalability of attack graphs, the authors of \cite{ou_scalable_2006} present a new representation scheme. Traditional attack graphs encode the entire network at each state, but the representation presented by the authors uses logical statements to represent a portion of the network at each node. This is called a logical attack graph. This approach led to the reduction of the generation process to quadratic time and reduced the number of nodes in the resulting graph to $\mathcal{O}({n}^2)$. However, this approach does require more analysis for identifying attack vectors. There have also been numerous approaches at generation improvement specific to attack graphs. As a means of improving scalability of attack graphs, the authors of \cite{ou_scalable_2006} present a new representation scheme. Traditional attack graphs encode the entire network at each state, but the representation presented by the authors uses logical statements to represent a portion of the network at each node. This is called a logical attack graph. This approach led to the reduction of the generation process to quadratic time and reduced the number of nodes in the resulting graph to $\mathcal{O}({n}^2)$. However, this approach does require more analysis for identifying attack vectors.
Another approach presented by the authors of \cite{cook_scalable_2016} represents a description of systems and their qualities and topologies as a state, with a queue of unexplored states. Another approach presented by the authors of \cite{cook_scalable_2016} represents a description of systems and their qualities and topologies as a state, with a queue of unexplored states.