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vonLaszewski-frontiers-citations.bib
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@article{awspcs:online,
author = {{AWS}},
title = {What is AWS ParallelCluster - AWS ParallelCluster},
note = {\url
{https://docs.aws.amazon.com/parallelcluster/latest/ug/what-is-aws-parallelcluster.html}},
month = oct,
year = 2024,
journal = {NA},
note = {(Accessed on 09/27/2024)}
}
@article{HPCpricing:online,
author = {{AWS}},
title = {HPC Workload Service – AWS Parallel Computing
Service Pricing – AWS},
note = {\url{https://aws.amazon.com/pcs/pricing/}},
month = oct,
year = 2024,
journal = {NA},
note = {(Accessed on 09/28/2024)}
}
@article{ec2ondemand:online,
author = {{AWS}},
author = {},
title = {EC2 On-Demand Instance Pricing – Amazon Web
Services},
note = {\url
{https://aws.amazon.com/ec2/pricing/on-demand/}},
month = oct,
year = 2024,
journal = {NA},
note = {(Accessed on 09/28/2024)}
}
@article{spot-instance:online,
author = {{AWS}},
title = {Savings from purchasing Spot Instances - Amazon
Elastic Compute Cloud},
note = {\url
{https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-savings.html}},
month = oct,
year = 2024,
note = {(Accessed on 09/28/2024)}
}
@article{spotSavings:online,
author = {{AWS}},
title = {Savings from purchasing Spot Instances - Amazon
Elastic Compute Cloud},
note = {\url
{https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-savings.html}},
month = oct,
year = 2024,
note = {(Accessed on 09/28/2024)}
}
@article{CFD:online,
author = {{AWS}},
title = {CFD simulation of aerodynamic forces on the DrivAer
car model: Impact of computational parameters},
note = {\url
{https://www.sciencedirect.com/science/article/pii/S0167610524000746}},
month = oct,
year = 2024,
note = {(Accessed on 10/24/2024)}
}
@article{awspcsblob1:online,
author = {{AWS}},
title = {You told us we needed to re-think HPC in the
cloud. So we did. | AWS HPC Blog},
note = {\url
{https://aws.amazon.com/blogs/hpc/you-told-us-we-needed-to-re-think-hpc-in-the-cloud-so-we-did/}},
month = oct,
year = 2024,
note = {(Accessed on 10/24/2024)}
}
@article{awspcsstorage:online,
author = {{AWS}},
title = {Using network file systems with AWS PCS - AWS PCS},
\note =
{\url{https://docs.aws.amazon.com/pcs/latest/userguide/working-with_file-systems.html}},
month = oct,
year = 2024,
note = {(Accessed on 10/24/2024)}
}
@article{yamldb,
author = {von Laszewski, Gregor},
title = {cloudmesh/yamldb},
note = {\url{https://github.com/cloudmesh/yamldb}},
year = {2020},
note = {(Accessed on 10/25/2024)}
}
@article{las-2022-hybrid,
title = {Hybrid Reusable Computational Analytics Workflow
Management with Cloudmesh},
author = {Gregor von Laszewski and J. P. Fleischer and
Geoffrey C. Fox},
year = 2022,
eprint = {2210.16941},
archivePrefix ={arXiv},
primaryClass = {cs.DC},
note = {\url{https://arxiv.org/abs/2210.16941}},
}
@INPROCEEDINGS{las-2022-templated,
author = {von Laszewski, Gregor and Fleischer, J.P. and Fox,
Geoffrey C. and Papay, Juri and Jackson, Sam and
Thiyagalingam, Jeyan},
booktitle = {2023 IEEE 19th International Conference on e-Science
(e-Science)},
title = {Templated Hybrid Reusable Computational Analytics
Workflow Management with Cloudmesh, Applied to the
Deep Learning MLCommons Cloudmask Application},
year = 2023,
pages = {1-6},
keywords = {Deep learning;Codes;Operating
systems;Linux;Benchmark
testing;History;Synchronization;Task
analysis;Artificial
intelligence;Monitoring;experiment workflow;task
workflow;hyperparameter workflow;high-performance
computing;batch queue management;workflow web
service;cloudmesh},
doi = {10.1109/e-Science58273.2023.10254942}
}
@article{www-cloudmesh-org,
author = {},
title = {Cloudmesh Version 4 },
howpublished =
{\url{https://cloudmesh.github.io/cloudmesh-manual/index.html}},
year = {},
note = {(Accessed on 10/25/2024)}
}
@article{www-aws-pricing,
author = {{AWS}},
title = {HPC Workload Service – AWS Parallel Computing
Service Pricing},
howpublished = {Web Page},
url = {https://aws.amazon.com/pcs/pricing/},
month = oct,
year = 2024
}
@article{prace-fact,
author = {},
title = {Fact-Sheet-PRACE-Access.pdf},
howpublished = {Web Page},
note = {\url
{https://prace-ri.eu/wp-content/uploads/Fact-Sheet-PRACE-Access.pdf}},
month = oct,
year = 2024
}
@article{www-prace,
author = {},
title = {PRACE HPC Infrastructure - PRACE},
howpublished = {Web Page},
note = {\url
{https://prace-ri.eu/prace-archive/infrastructure-support/prace-hpc-infrastructure/}},
month = oct,
year = 2024
}
@article{incommon,
author = {},
title = {Home Page - InCommon},
howpublished = {Web Page},
url = {https://incommon.org/},
month = oct,
year = 2024
}
@article{cylon,
title = {In-depth analysis on parallel processing patterns
for high-performance Dataframes},
journal = {Future Generation Computer Systems},
volume = 149,
pages = {250-264},
year = 2023,
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2023.07.007},
url =
{https://www.sciencedirect.com/science/article/pii/S0167739X23002595},
author = {Niranda Perera and Arup Kumar Sarker and Mills
Staylor and Gregor {von Laszewski} and Kaiying Shan
and Supun Kamburugamuve and Chathura Widanage and
Vibhatha Abeykoon and Thejaka Amila Kanewela and
Geoffrey Fox},
keywords = {Dataframes, High-performance computing, Data
engineering, Relational algebra, MPI, Distributed
Memory Parallel},
abstract = {The Data Science domain has expanded monumentally in
both research and industry communities during the
past decade, predominantly owing to the Big Data
revolution. Artificial Intelligence (AI) and Machine
Learning (ML) are bringing more complexities to data
engineering applications, which are now integrated
into data processing pipelines to process terabytes
of data. Typically, a significant amount of time is
spent on data preprocessing in these pipelines, and
hence improving its efficiency directly impacts the
overall pipeline performance. The community has
recently embraced the concept of Dataframes as the
de-facto data structure for data representation and
manipulation. However, the most widely used serial
Dataframes today (R, pandas) experience performance
limitations while working on even moderately large
data sets. We believe that there is plenty of room
for improvement by taking a look at this problem
from a high-performance computing point of view. In
a prior publication, we presented a set of parallel
processing patterns for distributed dataframe
operators and the reference runtime implementation,
Cylon [1]. In this paper, we are expanding on the
initial concept by introducing a cost model for
evaluating the said patterns. Furthermore, we
evaluate the performance of Cylon on the ORNL Summit
supercomputer.}
}
@article{cylon-radical,
title = {Design and Implementation of an Analysis Pipeline
for Heterogeneous Data},
author = {Arup Kumar Sarker and Aymen Alsaadi and Niranda
Perera and Mills Staylor and Gregor von Laszewski
and Matteo Turilli and Ozgur Ozan Kilic and Mikhail
Titov and Andre Merzky and Shantenu Jha and Geoffrey
Fox},
year = 2024,
eprint = {2403.15721},
archivePrefix ={arXiv},
primaryClass = {cs.DC},
url = {https://arxiv.org/abs/2403.15721},
}
@INPROCEEDINGS{eucalyptus,
author = {Nurmi, Daniel and Wolski, Rich and Grzegorczyk,
Chris and Obertelli, Graziano and Soman, Sunil and
Youseff, Lamia and Zagorodnov, Dmitrii},
booktitle = {2009 9th IEEE/ACM International Symposium on Cluster
Computing and the Grid},
title = {The Eucalyptus Open-Source Cloud-Computing System},
year = 2009,
pages = {124-131},
keywords = {Open source software;Cloud computing;Computer
interfaces;Grid computing;Resource
management;Instruments;Control systems;Virtual
machining;Application software;Software
maintenance;cloud computing;virtualization},
doi = {10.1109/CCGRID.2009.93}
}
@ARTICLE{opencirrus,
author = {Avetisyan, Arutyun I. and Campbell, Roy and Gupta,
Indranil and Heath, Michael T. and Ko, Steven Y. and
Ganger, Gregory R. and Kozuch, Michael A. and
O'Hallaron, David and Kunze, Marcel and Kwan, Thomas
T. and Lai, Kevin and Lyons, Martha and Milojicic,
Dejan S. and Lee, Hing Yan and Soh, Yeng Chai and
Ming, Ng Kwang and Luke, Jing-Yuan and Namgoong,
Han},
journal = {Computer},
title = {Open Cirrus: A Global Cloud Computing Testbed},
year = 2010,
volume = 43,
number = 4,
pages = {35-43},
keywords = {Cloud computing;Testing;Technological
innovation;Open Cirrus;Cloud computing;Distributed
computing;Systems engineering;Internet/Web},
doi = {10.1109/MC.2010.111}
}
@article{www-keydb,
author = {},
title = {KeyDB - The Faster Redis Alternative},
howpublished = {Web Page},
url = {https://docs.keydb.dev/},
month = sep,
year = 2024
}
@ARTICLE{las-frontiers-edu,
AUTHOR = {von Laszewski, Gregor and Fleischer, J. P. and
Knuuti, Robert and Fox, Geoffrey C. and Kolessar,
Jake and Butler, Thomas S. and Fox, Judy },
TITLE = {Opportunities for enhancing MLCommons efforts while
leveraging insights from educational MLCommons
earthquake benchmarks efforts},
JOURNAL = {Frontiers in High Performance Computing},
VOLUME = 1,
YEAR = 2023,
URL =
{https://www.frontiersin.org/journals/high-performance-computing/articles/10.3389/fhpcp.2023.1233877},
DOI = {10.3389/fhpcp.2023.1233877},
ISSN = {2813-7337},
}
@article{cloudmesh-ee,
author = {von Laszewski, Gregor},
title = {{Cloudmesh Experiment Executor}},
year = 2023,
month = sep,
note = {[Online; accessed 8. Sep. 2023]
\url{https://github.com/cloudmesh/cloudmesh-ee} }
},
url = {https://github.com/cloudmesh/cloudmesh-ee}
}
@article{cloudmesh-cc,
author = {von Laszewski, Gregor},
title = {{loudmesh Compute Coordinator}},
year = 2023,
month = sep,
note = {[Online; accessed 8. Sep. 2023]
\url{https://github.com/cloudmesh/cloudmesh-ee} }
},
url = {https://github.com/cloudmesh/cloudmesh-ee}
}
@article{uva-ondemand,
title = {{Open OnDemand {$\vert$} Research Computing}},
year = 2023,
month = sep,
note = {[Online; accessed 6. Sep. 2023]},
url =
{https://www.rc.virginia.edu/userinfo/rivanna/ood/overview}
}
@article{www-pep8,
title = {{PEP 8 {\textendash} Style Guide for Python Code
{$\vert$} peps.python.org}},
year = 2023,
month = sep,
note = {[Online; accessed 6. Sep. 2023]},
url = {https://peps.python.org/pep-0008}
}
@inproceedings{las-2023-escience,
address = {Limassol, Cyprus},
author = {von Laszewski, Gregor and J.P. Fleischer, J.P. and
Fox, Geoffrey C. and Juri Papay and Sam Jackson and
Jeyan Thiyagalingam},
booktitle = {eScience'23},
month = {October},
organization = {Second Workshop on Reproducible Workflows, Data, and
Security (ReWorDS 2022)},
title = {Templated Hybrid Reusable Computational Analytics
Workflow Management with Cloudmesh, Applied to the
Deep Learning MLCommons Cloudmask Application},
year = 2023
}
@article{energy-price,
title = {{Average energy prices for the United States,
regions, census divisions, and selected metropolitan
areas : Midwest Information Office : U.S. Bureau of
Labor Statistics}},
year = 2020,
month = dec,
note = {[Online; accessed 2. Sep. 2023]},
url =
{https://www.bls.gov/regions/midwest/data/averageenergyprices_selectedareas_table.htm}
}
@article{greenhouse-calc,
author = {Oar},
title = {{Greenhouse Gas Equivalencies Calculator}},
journal = {US EPA},
year = 2023,
month = jul,
url =
{https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator}
}
@inproceedings{las-22-mlcommons-science,
address = {Cham},
author = {Thiyagalingam, Jeyan and von Laszewski, Gregor and
Yin, Junqi and Emani, Murali and Papay, Juri and
Barrett, Gregg and Luszczek, Piotr and Tsaris,
Aristeidis and Kirkpatrick, Christine and Wang,
Feiyi and Gibbs, Tom and Vishwanath, Venkatram and
Shankar, Mallikarjun and Fox, Geoffrey and Hey,
Tony},
booktitle = {High Performance Computing. ISC High Performance
2022 International Workshops},
editor = {Anzt, Hartwig and Bienz, Amanda and Luszczek, Piotr
and Baboulin, Marc},
pages = {47--64},
publisher = {Springer International Publishing},
title = {{AI Benchmarking for Science: Efforts from the
MLCommons Science Working Group}},
year = 2022,
abstract = {With machine learning (ML) becoming a transformative
tool for science, the scientific community needs a
clear catalogue of ML techniques, and their relative
benefits on various scientific problems, if they
were to make significant advances in science using
AI. Although this comes under the purview of
benchmarking, conventional benchmarking initiatives
are focused on performance, and as such, science,
often becomes a secondary criteria.},
isbn = {978-3-031-23220-6},
}
@article{dongarra1997top500,
author = {Dongarra, Jack J and Meuer, Hans W and Strohmaier,
Erich and others},
journal = {Supercomputer},
pages = {89--111},
publisher = {ASFRA BV},
title = {{TOP500 Supercomputer Sites}},
volume = 13,
year = 1997,
}
@article{www-top500,
journal = {Web page},
key = {Top500},
note = {\url{https://www.top500.org/} [Accessed April 13,
2023]},
title = {Homepage},
year = 2023,
}
@article{www-mlcommons,
journal = {Web page},
key = {MLCommons},
month = apr,
note = {\url{https://mlcommons.org/} [Accessed April 13,
2023]},
title = {{Machine learning innovation to benefit everyone}},
year = 2023,
url = {https://mlcommons.org/},
}
@article{mlperf-training,
author = {Mattson, Peter and Cheng, Christine and Coleman,
Cody and Diamos, Greg and Micikevicius, Paulius and
Patterson, David and Tang, Hanlin and Wei, Gu-Yeon
and Bailis, Peter and Bittorf, Victor and Brooks,
David and Chen, Dehao and Dutta, Debojyoti and
Gupta, Udit and Hazelwood, Kim and Hock, Andrew and
Huang, Xinyuan and Ike, Atsushi and Jia, Bill and
Kang, Daniel and Kanter, David and Kumar, Naveen and
Liao, Jeffery and Ma, Guokai and Narayanan, Deepak
and Oguntebi, Tayo and Pekhimenko, Gennady and
Pentecost, Lillian and Reddi, Vijay Janapa and
Robie, Taylor and John, Tom St. and Tabaru,
Tsuguchika and Wu, Carole-Jean and Xu, Lingjie and
Yamazaki, Masafumi and Young, Cliff and Zaharia,
Matei},
journal = {arXiv},
publisher = {arXiv},
title = {{MLPerf Training Benchmark}},
year = 2019,
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