135 lines
5.8 KiB
BibTeX
135 lines
5.8 KiB
BibTeX
@phdthesis{phd:gruber,
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url = "http://www.theses.fr/2019GREAM071",
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title = "Performance Debugging Toolbox for Binaries: Sensitivity Analysis and Dependence Profiling",
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author = "Gruber, Fabian",
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school = "Université Grenoble Alpes",
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year = "2019",
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note = "2019GREAM071",
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url = "http://www.theses.fr/2019GREAM071/document",
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}
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@article{kendalltau,
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title={A new measure of rank correlation},
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author={Kendall, Maurice G},
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journal={Biometrika},
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volume={30},
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number={1/2},
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pages={81--93},
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year={1938},
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publisher={JSTOR}
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}
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@manual{ref:intel64_software_dev_reference_vol1,
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title = {Intel® 64 and IA-32 Architectures Software Developer’s Manual,
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volume 1},
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organization = {Intel Corporation},
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year = {2023},
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month = {June},
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}
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@manual{ref:amd64_architecture_dev_reference_vol2,
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title = {AMD64 Architecture Programmer’s Manual, volume 2},
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organization = {AMD},
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year = {2023},
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month = {June},
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}
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@incollection{grid5000,
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title = {Adding Virtualization Capabilities to the {Grid'5000} Testbed},
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author = {Balouek, Daniel and Carpen Amarie, Alexandra and Charrier, Ghislain and Desprez, Fr{\'e}d{\'e}ric and Jeannot, Emmanuel and Jeanvoine, Emmanuel and L{\`e}bre, Adrien and Margery, David and Niclausse, Nicolas and Nussbaum, Lucas and Richard, Olivier and P{\'e}rez, Christian and Quesnel, Flavien and Rohr, Cyril and Sarzyniec, Luc},
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booktitle = {Cloud Computing and Services Science},
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publisher = {Springer International Publishing},
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pages = {3-20},
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volume = {367},
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editor = {Ivanov, Ivan I. and van Sinderen, Marten and Leymann, Frank and Shan, Tony },
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series = {Communications in Computer and Information Science },
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isbn = {978-3-319-04518-4 },
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doi = {10.1007/978-3-319-04519-1\_1 },
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year = {2013},
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}
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@article{hcluster_ward,
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ISSN = {01621459},
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URL = {http://www.jstor.org/stable/2282967},
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abstract = {A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale ($n > 100$) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n - 1 mutually exclusive sets by considering the union of all possible n(n - 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.},
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author = {Joe H. Ward},
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journal = {Journal of the American Statistical Association},
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number = {301},
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pages = {236--244},
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publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
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title = {Hierarchical Grouping to Optimize an Objective Function},
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urldate = {2023-09-15},
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volume = {58},
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year = {1963}
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}
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@article{hcluster_silhouette,
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title = {Silhouettes: A graphical aid to the interpretation and validation of cluster analysis},
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journal = {Journal of Computational and Applied Mathematics},
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volume = {20},
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pages = {53-65},
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year = {1987},
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issn = {0377-0427},
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doi = {https://doi.org/10.1016/0377-0427(87)90125-7},
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url = {https://www.sciencedirect.com/science/article/pii/0377042787901257},
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author = {Peter J. Rousseeuw},
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keywords = {Graphical display, cluster analysis, clustering validity, classification},
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abstract = {A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.}
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}
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@misc{a72_doc,
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title={Cortex A-72},
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author={{ARM}},
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howpublished={\url{https://developer.arm.com/Processors/Cortex-A72}}
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}
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@manual{ref:a72_optim,
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title = {Cortex-A72 Software Optimization Guide},
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organization = {ARM},
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year = {2015},
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month = {March},
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}
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@misc{agnerfog_skl_front4,
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title={Discussion on blogpost},
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author={Fog, Agner},
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year=2016,
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howpublished={\url{https://www.agner.org/optimize/blog/read.php?i=581}}
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}
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@INPROCEEDINGS{fugaku_arm,
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author={Matsuoka, Satoshi},
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booktitle={2021 Symposium on VLSI Circuits},
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title={Fugaku and A64FX: the First Exascale Supercomputer and its Innovative Arm CPU},
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year={2021},
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volume={},
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number={},
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pages={1-3},
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doi={10.23919/VLSICircuits52068.2021.9492415}
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}
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@misc{fugaku_top500,
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title={Supercomputer Fugaku retains first place worldwide in HPCG and Graph500 rankings},
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year=2022,
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month=November,
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author={{Fujitsu Limited}},
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howpublished={\url{https://www.fujitsu.com/global/about/resources/news/press-releases/2022/1115-01.html}}
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}
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@misc{marenostrum4_arm,
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title={Technical information on the MareNostrum 4 supercomputer's ARM cluster},
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author={{Barcelona Supercomputing Center}},
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year=2020,
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howpublished={\url{https://www.bsc.es/innovation-and-services/technical-information-cte-arm}}
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}
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@misc{arm_mobile,
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title={Together, we are building the future of computing, on Arm},
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author={Rene Haas},
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organization = {ARM},
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year=2023,
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month=September,
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howpublished={\url{https://www.arm.com/company/news/2023/09/building-the-future-of-computing-on-arm}},
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}
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