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Noah L. Schrick 2022-03-27 18:14:44 -05:00
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Bibliography.bib
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@ -6,6 +6,8 @@
@misc{noauthor_overview_nodate, @misc{noauthor_overview_nodate,
title = {An {Overview} of the {Parallel} {Boost} {Graph} {Library} - 1.75.0}, title = {An {Overview} of the {Parallel} {Boost} {Graph} {Library} - 1.75.0},
authors = {Edmonds, Nick and Gregor, Douglas and Lumsdaine, Andrew},
year = {2009},
url = {https://www.boost.org/doc/libs/1_75_0/libs/graph_parallel/doc/html/overview.html}, url = {https://www.boost.org/doc/libs/1_75_0/libs/graph_parallel/doc/html/overview.html},
} }
@ -16,7 +18,8 @@
} }
@misc{noauthor_boost_nodate, @misc{noauthor_boost_nodate,
title = {The {Boost} {Graph} {Library} - 1.75.0}, title = {The {Boost} {Graph} {Library}, vers. 1.75.0},
author = {Siek, Jeremy and Lee, Lie-Quan and Lumsdaine, Andrew},
url = {https://www.boost.org/doc/libs/1_75_0/libs/graph/doc/index.html}, url = {https://www.boost.org/doc/libs/1_75_0/libs/graph/doc/index.html},
} }

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Chapter2.tex
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@ -13,8 +13,8 @@ nodes and edges, graph data structures suffer from poor cache locality, and memo
\TUsection{Graph Generation Improvements} \label{sec:gen_improv} \TUsection{Graph Generation Improvements} \label{sec:gen_improv}
For architectural and hardware techniques for generation improvement, the authors of \cite{ainsworth_graph_2016} discuss the high cache miss rate, and how general prefetching For architectural and hardware techniques for generation improvement, the authors of \cite{ainsworth_graph_2016} discuss the high cache miss rate, and how general prefetching
does not increase the prediction rate due to nonsequential graph structures and data-dependent access patterns. However, the authors continue to discuss that the generation does not increase the prediction rate due to nonsequential graph structures and data-dependent access patterns. However, the authors continue to discuss that generation
algorithm is known in advance, so explicit tuning of the hardware prefetcher to follow the traversal order pattern can lead to better performance. The authors were able to achieve algorithms are known in advance, so explicit tuning of the hardware prefetcher to follow the traversal order pattern can lead to better performance. The authors were able to achieve
over 2x performance improvement of a breadth-first search approach with this method. Another hardware approach is to make use of accelerators. The authors of \cite{yao_efficient_2018} over 2x performance improvement of a breadth-first search approach with this method. Another hardware approach is to make use of accelerators. The authors of \cite{yao_efficient_2018}
present an approach for minimizing the slowdown caused by the underlying graph atomic functions. By using the atomic function patterns, the authors utilized pipeline stages where vertex present an approach for minimizing the slowdown caused by the underlying graph atomic functions. By using the atomic function patterns, the authors utilized pipeline stages where vertex
updates can be processed in parallel dynamically. Other works, such as those by the authors of \cite{zhang_boosting_2017} and \cite{dai_fpgp_2016}, leverage field-programmable gate arrays updates can be processed in parallel dynamically. Other works, such as those by the authors of \cite{zhang_boosting_2017} and \cite{dai_fpgp_2016}, leverage field-programmable gate arrays
@ -23,7 +23,7 @@ on-chip block RAM, or even leveraging Hybrid Memory Cubes for optimizing paralle
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 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 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} discuss the usage of distributed adjacency lists for assigning vertices to workers. The authors of \cite{liakos_memory-optimized_2016} and \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 Boost Graph Library \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 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}. \cite{noauthor_overview_nodate}, \cite{noauthor_boost_nodate}.

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Schrick-Noah_MS-Thesis.bbl
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@ -103,10 +103,11 @@ J.~Balaji and R.~Sunderraman, ``Graph {Topology} {Abstraction} for
May 2016. May 2016.
\bibitem{noauthor_overview_nodate} \bibitem{noauthor_overview_nodate}
``An {Overview} of the {Parallel} {Boost} {Graph} {Library} - 1.75.0.'' ``An {Overview} of the {Parallel} {Boost} {Graph} {Library} - 1.75.0,'' 2009.
\bibitem{noauthor_boost_nodate} \bibitem{noauthor_boost_nodate}
``The {Boost} {Graph} {Library} - 1.75.0.'' J.~Siek, L.-Q. Lee, and A.~Lumsdaine, ``The {Boost} {Graph} {Library}, vers.
1.75.0.''
\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

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Schrick-Noah_MS-Thesis.blg
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@ -10,42 +10,42 @@ A level-1 auxiliary file: Chapter6.aux
The style file: ieeetr.bst The style file: ieeetr.bst
Database file #1: Bibliography.bib Database file #1: Bibliography.bib
Warning--entry type for "j_hale_compliance_nodate" isn't style-file defined Warning--entry type for "j_hale_compliance_nodate" isn't style-file defined
--line 272 of file Bibliography.bib --line 275 of file Bibliography.bib
Warning--entry type for "EUdataregulations2018" isn't style-file defined Warning--entry type for "EUdataregulations2018" isn't style-file defined
--line 1250 of file Bibliography.bib --line 1253 of file Bibliography.bib
Warning--empty journal in ou_scalable_2006 Warning--empty journal in ou_scalable_2006
Warning--empty school in cook_rage_2018 Warning--empty school in cook_rage_2018
Warning--empty school in louthan_hybrid_2011 Warning--empty school in louthan_hybrid_2011
You've used 30 entries, You've used 30 entries,
1876 wiz_defined-function locations, 1876 wiz_defined-function locations,
622 strings with 9004 characters, 624 strings with 9065 characters,
and the built_in function-call counts, 6493 in all, are: and the built_in function-call counts, 6551 in all, are:
= -- 608 = -- 612
> -- 251 > -- 261
< -- 0 < -- 0
+ -- 97 + -- 100
- -- 67 - -- 70
* -- 418 * -- 425
:= -- 895 := -- 911
add.period$ -- 29 add.period$ -- 30
call.type$ -- 30 call.type$ -- 30
change.case$ -- 28 change.case$ -- 28
chr.to.int$ -- 0 chr.to.int$ -- 0
cite$ -- 33 cite$ -- 33
duplicate$ -- 333 duplicate$ -- 333
empty$ -- 695 empty$ -- 695
format.name$ -- 67 format.name$ -- 70
if$ -- 1596 if$ -- 1606
int.to.chr$ -- 0 int.to.chr$ -- 0
int.to.str$ -- 30 int.to.str$ -- 30
missing$ -- 19 missing$ -- 19
newline$ -- 96 newline$ -- 96
num.names$ -- 25 num.names$ -- 26
pop$ -- 147 pop$ -- 148
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substring$ -- 347 substring$ -- 347
swap$ -- 108 swap$ -- 108
@ -54,7 +54,7 @@ text.prefix$ -- 0
top$ -- 0 top$ -- 0
type$ -- 0 type$ -- 0
warning$ -- 3 warning$ -- 3
while$ -- 59 while$ -- 60
width$ -- 32 width$ -- 32
write$ -- 248 write$ -- 250
(There were 5 warnings) (There were 5 warnings)

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Schrick-Noah_MS-Thesis.log
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