Giulio Malenza
PhD Student
Computer Science Department, University of Turin
Parallel Computing group
Via Pessinetto 12, 10149 Torino – Italy
E-mail: giulio.malenza@unito.it
Short Bio
Giulio Malenza is a Ph.D. student in computer science at the University of Turin. He has a master in High Performance Computing, a master degree in Mathematical Engineering and a bachelor degree in Physics.
Fields of interest:
- High Performance Computing
- Parallel Computing
- Scientific Computing
Publications
2024
Giulio Malenza, Valentina Cesare, Marco Aldinucci, Ugo Becciani, Alberto Vecchiato
Toward HPC application portability via C++ PSTL: the Gaia AVU-GSR code assessment Journal Article
In: The Journal of Supercomputing, 2024, ISSN: 09208542.
Abstract | Links | BibTeX | Tags: eupex, HPC, icsc
@article{24:jsupe:Gaia,
title = {Toward HPC application portability via C++ PSTL: the Gaia AVU-GSR code assessment},
author = {Giulio Malenza and Valentina Cesare and Marco Aldinucci and Ugo Becciani and Alberto Vecchiato},
doi = {10.1007/s11227-024-06011-1},
issn = {09208542},
year = {2024},
date = {2024-03-01},
journal = {The Journal of Supercomputing},
publisher = {Springer},
abstract = {The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of $$sim10^8$$stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture.},
keywords = {eupex, HPC, icsc},
pubstate = {published},
tppubtype = {article}
}
The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of $$sim10^8$$stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture.
Marco Edoardo Santimaria, Samuele Fonio, Giulio Malenza, Iacopo Colonnelli, Marco Aldinucci
Benchmarking Parallelization Models through Karmarkar Interior-point method Proceedings Article
In: Chis, Horacio González-Vélez Adriana E. (Ed.): 2024 32nd Euromicro International Conference on Parallel, Distributed and Network-based Processing, pp. 1–8, IEEE, Dublin, Ireland, 2024, ISSN: 2377-5750.
Abstract | Links | BibTeX | Tags: HPC, icsc
@inproceedings{24:pdp:karmarkar,
title = {Benchmarking Parallelization Models through Karmarkar Interior-point method},
author = {Marco Edoardo Santimaria and Samuele Fonio and Giulio Malenza and Iacopo Colonnelli and Marco Aldinucci},
editor = {Horacio González-Vélez Adriana E. Chis},
url = {https://hdl.handle.net/2318/1964571},
doi = {10.1109/PDP62718.2024.00010},
issn = {2377-5750},
year = {2024},
date = {2024-03-01},
booktitle = {2024 32nd Euromicro International Conference on Parallel, Distributed and Network-based Processing},
pages = {1–8},
publisher = {IEEE},
address = {Dublin, Ireland},
abstract = {Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar's Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar's algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations},
keywords = {HPC, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar's Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar's algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations
2023
Gianluca Mittone, Giulio Malenza, Marco Aldinucci, Robert Birke
Distributed Edge Inference: an Experimental Study on Multiview Detection Proceedings Article
In: UCC '23: Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing Companion, Taormina, Italy, 2023, (eupilot, icsc, In press).
Abstract | Links | BibTeX | Tags: ai
@inproceedings{23:mittone:multiview,
title = {Distributed Edge Inference: an Experimental Study on Multiview Detection},
author = {Gianluca Mittone and Giulio Malenza and Marco Aldinucci and Robert Birke},
url = {https://iris.unito.it/handle/2318/1950083},
year = {2023},
date = {2023-12-01},
booktitle = {UCC '23: Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing Companion},
address = {Taormina, Italy},
institution = {Computer Science Department, University of Torino},
abstract = {Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum others to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.},
note = {eupilot, icsc, In press},
keywords = {ai},
pubstate = {published},
tppubtype = {inproceedings}
}
Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum others to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.
Talks
2024
Iacopo Colonnelli, Robert Birke, Giulio Malenza, Gianluca Mittone, Alberto Mulone, Marco Aldinucci
Cross-Facility Federated Learning - Part II Miscellaneous
2024, (Invited talk).
Links | BibTeX | Tags: eupex, icsc, space
@misc{24:ic:elise:xffl,
title = {Cross-Facility Federated Learning - Part II},
author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/7HonBpcWPxotXLX},
year = {2024},
date = {2024-06-01},
address = {Helsinki, Finland},
note = {Invited talk},
keywords = {eupex, icsc, space},
pubstate = {published},
tppubtype = {misc}
}
Giulio Malenza, Marco Edoardo Santimaria
Benchmarking Parallelization Models through Karmarkar`s algorithm Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: HPC, icsc
@misc{24:pdp:karmarkartalk,
title = {Benchmarking Parallelization Models through Karmarkar`s algorithm},
author = {Giulio Malenza and Marco Edoardo Santimaria},
url = {https://datacloud.di.unito.it/index.php/s/JjKcAJpYS7ctX9r},
year = {2024},
date = {2024-03-01},
address = {Dublin, Irelans},
abstract = {Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar’s Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar’s algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations},
keywords = {HPC, icsc},
pubstate = {published},
tppubtype = {misc}
}
Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar’s Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar’s algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations
2023
Iacopo Colonnelli, Robert Birke, Giulio Malenza, Gianluca Mittone, Alberto Mulone, Marco Aldinucci, Valerio Basile, Marco Antonio Stranisci, Viviana Patti, Jeroen Galjaard, Lydia Y. Chen, Sanzio Bassini, Massimiliano Guarrasi, Gabriella Scipione, Jan Martinovič, Vit Vondrák
Cross-Facility Federated Learning Miscellaneous
1st EuroHPC User Day, 2023.
Links | BibTeX | Tags: across, ai, eupex, eupilot, HPC
@misc{23:eurohpc,
title = {Cross-Facility Federated Learning},
author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Marco Aldinucci and Valerio Basile and Marco Antonio Stranisci and Viviana Patti and Jeroen Galjaard and Lydia Y. Chen and Sanzio Bassini and Massimiliano Guarrasi and Gabriella Scipione and Jan Martinovič and Vit Vondrák},
url = {https://datacloud.di.unito.it/index.php/s/DDAz4QkJP3WZ68M},
year = {2023},
date = {2023-12-01},
address = {Bruxelles, Belgium},
howpublished = {1st EuroHPC User Day},
keywords = {across, ai, eupex, eupilot, HPC},
pubstate = {published},
tppubtype = {misc}
}
Gianluca Mittone, Giulio Malenza, Marco Aldinucci, Robert Birke
Distributed Edge Inference: an Experimental Study on Multiview Detection Miscellaneous
The 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023), 2023.
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@misc{23:ucc:multiview,
title = {Distributed Edge Inference: an Experimental Study on Multiview Detection},
author = {Gianluca Mittone and Giulio Malenza and Marco Aldinucci and Robert Birke},
url = {https://datacloud.di.unito.it/index.php/s/XfjNZEPSNfSKPFr},
year = {2023},
date = {2023-12-01},
address = {Taormina, Italy},
abstract = {Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum others to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.},
howpublished = {The 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023)},
keywords = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {misc}
}
Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum others to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.
Giulio Malenza, Valentina Cesare, Marco Aldinucci
Performance portability in HPC: the Gaia use-case. Miscellaneous
2nd Italian Conference on Big Data and Data Science (ITADATA 2023), 2023.
@misc{23:GAIA:bigHPC,
title = {Performance portability in HPC: the Gaia use-case.},
author = {Giulio Malenza and Valentina Cesare and Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/RqcZpizFtC9toFq},
year = {2023},
date = {2023-09-01},
address = {Naples, Italy},
howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},
keywords = {icsc},
pubstate = {published},
tppubtype = {misc}
}
Giulio Malenza
Building an accelerated OpenFOAM Proof-of-Concept application using Modern C++. Miscellaneous
18th OpenFOAM Workshop 2023, Genova, 2023.
@misc{23:OF:genova,
title = {Building an accelerated OpenFOAM Proof-of-Concept application using Modern C++.},
author = {Giulio Malenza},
url = {https://datacloud.di.unito.it/index.php/s/mB6omsDB8ERBkGW},
year = {2023},
date = {2023-07-01},
address = {Genova, Italy},
howpublished = {18th OpenFOAM Workshop 2023, Genova},
keywords = {icsc},
pubstate = {published},
tppubtype = {misc}
}