-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathviviani_cv.bib
executable file
·190 lines (180 loc) · 11.2 KB
/
viviani_cv.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
@inproceedings{hpdc_keynote_2024,
author = {Viviani, Paolo},
title = {Demistifying HPC-Quantum integration: it's all about scheduling},
year = {2024},
isbn = {9798400706431},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3659996.3673223},
doi = {10.1145/3659996.3673223},
booktitle = {Proceedings of the 2024 Workshop on High Performance and Quantum Computing Integration},
pages = {1–3},
numpages = {3},
keywords = {quantum computing, HPC, parallel programming models, job scheduling, resource allocation},
location = {Pisa, Italy},
series = {HPQCI '24}
}
@inproceedings{vercellinoBBQmISParallelQuantum2023,
title = {{{BBQ-mIS}}: {{A Parallel Quantum Algorithm}} for {{Graph Coloring Problems}}},
shorttitle = {{{BBQ-mIS}}},
booktitle = {2023 {{IEEE International Conference}} on {{Quantum Computing}} and {{Engineering}} ({{QCE}})},
author = {Vercellino, Chiara and Vitali, Giacomo and Viviani, Paolo and Giusto, Edoardo and Scionti, Alberto and Scarabosio, Andrea and Terzo, Olivier and Montrucchio, Bartolomeo},
year = {2023},
month = sep,
pages = {141--147},
publisher = {{IEEE}},
address = {{Bellevue, WA, USA}},
doi = {10.1109/QCE57702.2023.10198},
urldate = {2023-12-01},
isbn = {9798350343236}
}
@InProceedings{10.1007/978-3-031-43427-3_23,
author="Viviani, Paolo
and Gesmundo, Ilaria
and Ghinato, Elios
and Agudelo-Toro, Andres
and Vercellino, Chiara
and Vitali, Giacomo
and Bergamasco, Letizia
and Scionti, Alberto
and Ghislieri, Marco
and Agostini, Valentina
and Terzo, Olivier
and Scherberger, Hansj{\"o}rg",
editor="De Francisci Morales, Gianmarco
and Perlich, Claudia
and Ruchansky, Natali
and Kourtellis, Nicolas
and Baralis, Elena
and Bonchi, Francesco",
title="Deep Learning for Real-Time Neural Decoding of Grasp",
booktitle="Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="379--393",
abstract="Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this context, this work starts from a specific pre-existing dataset of neural recordings from monkey motor cortex and presents a Deep Learning-based approach to the decoding of neural signals for grasp type classification. Specifically, we propose here an approach that exploits LSTM networks to classify time series containing neural data (i.e., spike trains) into classes representing the object being grasped.",
isbn="978-3-031-43427-3",
doi = {10.1007/978-3-031-43427-3_23}
}
@article{vercellino_fgcs_23,
title = {A Machine Learning Approach for an HPC Use Case: the Jobs Queuing Time Prediction},
journal = {Future Generation Computer Systems},
volume = {143},
pages = {215-230},
year = {2023},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2023.01.020},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X23000274},
author = {Chiara Vercellino and Alberto Scionti and Giuseppe Varavallo and Paolo Viviani and Giacomo Vitali and Olivier Terzo},
keywords = {High performance computing, Queues, Batch scheduler, Automatism, Machine learning, Uncertainty quantification},
abstract = {High-Performance Computing (HPC) domain provided the necessary tools to support the scientific and industrial advancements we all have seen during the last decades. HPC is a broad domain targeting to provide both software and hardware solutions as well as envisioning methodologies that allow achieving goals of interest, such as system performance and energy efficiency. In this context, supercomputers have been the vehicle for developing and testing the most advanced technologies since their first appearance. Unlike cloud computing resources that are provided to the end-users in an on-demand fashion in the form of virtualized resources (i.e., virtual machines and containers), supercomputers’ resources are generally served through State-of-the-Art batch schedulers (e.g., SLURM, PBS, LSF, HTCondor). As such, the users submit their computational jobs to the system, which manages their execution with the support of queues. In this regard, predicting the behaviour of the jobs in the batch scheduler queues becomes worth it. Indeed, there are many cases where a deeper knowledge of the time experienced by a job in a queue (e.g., the submission of check-pointed jobs or the submission of jobs with execution dependencies) allows exploring more effective workflow orchestration policies. In this work, we focused on applying machine learning (ML) techniques to learn from the historical data collected from the queuing system of real supercomputers, aiming at predicting the time spent on a queue by a given job. Specifically, we applied both unsupervised learning (UL) and supervised learning (SL) techniques to define the most effective features for the prediction task and the actual prediction of the queue waiting time. For this purpose, two approaches have been explored: on one side, the prediction of ranges on jobs’ queuing times (classification approach) and, on the other side, the prediction of the waiting time at the minutes level (regression approach). Experimental results highlight the strong relationship between the SL models’ performances and the way the dataset is split. At the end of the prediction step, we present the uncertainty quantification approach, i.e., a tool to associate the predictions with reliability metrics, based on variance estimation.}
}
@article{savioAcceleratingLegacyApplications2022,
title = {Accelerating Legacy Applications with Spatial Computing Devices},
author = {Savio, Paolo and Scionti, Alberto and Vitali, Giacomo and Viviani, Paolo and Vercellino, Chiara and Terzo, Olivier and Nguyen, Huy-Nam and Magarielli, Donato and Spano, Ennio and Marconcini, Michele and Poli, Francesco},
year = {2022},
month = nov,
journal = {The Journal of Supercomputing},
issn = {0920-8542, 1573-0484},
doi = {10.1007/s11227-022-04925-2},
langid = {english}
}
@inproceedings{vercellinoNeuralpoweredUnitDisk2022,
title = {Neural-Powered Unit Disk Graph Embedding: Qubits Connectivity for Some {{QUBO}} Problems},
shorttitle = {Neural-Powered Unit Disk Graph Embedding},
booktitle = {2022 {{IEEE International Conference}} on {{Quantum Computing}} and {{Engineering}} ({{QCE}})},
author = {Vercellino, Chiara and Viviani, Paolo and Vitali, Giacomo and Scionti, Alberto and Scarabosio, Andrea and Terzo, Olivier and Giusto, Edoardo and Montrucchio, Bartolomeo},
year = {2022},
month = sep,
pages = {186--196},
publisher = {{IEEE}},
address = {{Broomfield, CO, USA}},
doi = {10.1109/QCE53715.2022.00038},
isbn = {978-1-66549-113-6}
}
@incollection{vivianiTamingMultinodeAccelerated2022,
address = {Cham},
title = {Taming {Multi}-node {Accelerated} {Analytics}: {An} {Experience} in {Porting} {MATLAB} to {Scale} with {Python}},
volume = {497},
isbn = {978-3-031-08811-7 978-3-031-08812-4},
shorttitle = {Taming {Multi}-node {Accelerated} {Analytics}},
url = {https://link.springer.com/10.1007/978-3-031-08812-4_20},
language = {en},
urldate = {2022-06-23},
booktitle = {Complex, {Intelligent} and {Software} {Intensive} {Systems}},
publisher = {Springer International Publishing},
author = {Viviani, Paolo and Vitali, Giacomo and Lengani, Davide and Scionti, Alberto and Vercellino, Chiara and Terzo, Olivier},
editor = {Barolli, Leonard},
month = {jun},
year = {2022},
doi = {10.1007/978-3-031-08812-4_20},
note = {Series Title: Lecture Notes in Networks and Systems},
pages = {200--210},
file = {Viviani et al. - 2022 - Taming Multi-node Accelerated Analytics An Experi.pdf:/Users/pvi/Documents/Zotero/storage/WT8F3ICS/Viviani et al. - 2022 - Taming Multi-node Accelerated Analytics An Experi.pdf:application/pdf},
}
@incollection{sciontiDistributedHPCResources2022,
title = {Distributed {{HPC Resources Orchestration}} for {{Supporting Large-Scale Workflow Execution}}},
shorttitle = {{{HPC}}, {{Big Data}}, and {{AI Convergence Towards Exascale}}},
booktitle = {{{HPC}}, {{Big Data}}, and {{AI Convergence Towards Exascale}}: {{Challenge}} and {{Vision}}},
author = {Scionti, Alberto and Viviani, Paolo and Vitali, Giacomo and Vercellino, Chiara and Terzo, Olivier and Hachinger, Stephan and Vojacek, Luk{\'a}{\v s}},
year = {2022},
month = jan,
edition = {First},
pages = {23},
publisher = {{CRC Press}},
address = {{New York}},
doi = {10.1201/9781003176664},
isbn = {978-1-00-317666-4},
langid = {english}
}
@inproceedings{19:deeplearn:pdp,
Address = {Pavia, Italy},
Author = {Paolo Viviani and Maurizio Drocco and Daniele Baccega and Iacopo Colonnelli and Marco Aldinucci},
Booktitle = {Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
Date-Modified = {2019-03-22 22:49:35 +0100},
Doi = {10.1109/EMPDP.2019.8671552},
Keywords = {deep learning, distributed computing, machine learning, large scale, C++},
Pages = {124-131},
Publisher = {IEEE},
Title = {Deep Learning at Scale},
Url = {https://iris.unito.it/retrieve/handle/2318/1695211/487778/19_deeplearning_PDP.pdf},
Year = {2019},
Bdsk-Url-1 = {https://iris.unito.it/retrieve/handle/2318/1695211/487778/19_deeplearning_PDP.pdf}}
@inproceedings{18:hpc4ai_acm_CF,
Address = {Ischia, Italy},
Author = {Marco Aldinucci and Sergio Rabellino and Marco Pironti and Filippo Spiga and Paolo Viviani and Maurizio Drocco and Marco Guerzoni and Guido Boella and Marco Mellia and Paolo Margara and Idillio Drago and Roberto Marturano and Guido Marchetto and Elio Piccolo and Stefano Bagnasco and Stefano Lusso and Sara Vallero and Giuseppe Attardi and Alex Barchiesi and Alberto Colla and Fulvio Galeazzi},
Booktitle = {ACM Computing Frontiers},
Date-Added = {2018-04-21 14:18:48 +0000},
Date-Modified = {2018-04-21 14:26:05 +0000},
Doi = {10.1145/3203217.3205340},
Url = {http://alpha.di.unito.it/storage/papers/2018_hpc4ai_ACM_CF.pdf},
Keywords = {hpc4ai, c3s},
Month = may,
Title = {HPC4AI, an AI-on-demand federated platform endeavour},
Year = {2018}}
@inproceedings{svd:pdp:18,
Address = {Cambridge, United Kingdom},
Author = {Paolo Viviani and Maurizio Drocco and Marco Aldinucci},
Booktitle = {Proc. of 26th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
Date-Modified = {2018-01-30 11:07:31 +0000},
Doi = {},
Keywords = {svd, big data, linear algebra},
Pages = {},
Publisher = {IEEE},
Title = {Scaling Dense Linear Algebra on Multicore and Beyond: a Survey},
Url = {https://iris.unito.it/retrieve/handle/2318/1659340/387685/preprint_aperto.pdf},
Year = {2018}}
@inproceedings{17:sac:armadillo,
Address = {Marrakesh, Morocco},
Author = {Paolo Viviani and Massimo Torquati and Marco Aldinucci and Roberto d'Ippolito},
Booktitle = {In proc. of the 32nd ACM Symposium on Applied Computing (SAC)},
Date-Added = {2016-08-19 21:47:45 +0000},
Date-Modified = {2017-06-13 15:54:43 +0000},
Keywords = {nvidia, repara, rephrase, itea2},
Month = apr,
Pages = {1566--1573},
Title = {Multiple back-end support for the Armadillo linear algebra interface},
Url = {https://iris.unito.it/retrieve/handle/2318/1626229/299089/armadillo_4aperto.pdf},
Year = {2017},
Bdsk-Url-1 = {https://iris.unito.it/retrieve/handle/2318/1626229/299089/armadillo_4aperto.pdf}}