Paolo Viviani

Paolo Viviani
PhD Student
Computer Science Department, University of Turin
Parallel Computing group
Via Pessinetto 12, 10149 Torino – Italy icona mappa
E-mail: pviviani AT di.unito.it

Short Bio

Paolo Viviani is a PhD student in Computer Science at University of Turin. He holds a grant founded by the Noesis Solution NV. He main research topics are the development and deployment of machine learning methodologies on heterogeneous hardware and low-power and cost-effective computing architectures.

Fields of interest:

  • Development and deployment of machine learning methodologies on heterogeneous hardware.
  • Cloud, containers and virtualization for HPC.
  • Low-power and cost-effective computing architectures.

Noesis’ technical contact for the following European Research projects:

  • MACH: MAssive Calculations on Hybrid systems, ITEA2 project 12002. The goal of the project is to develop a DSeL and a computation framework that allows to access hybrid hardware acceleration without specific expertise.
  • Fortissimo 2:Horizon 2020-FoF-2015 project. FF2 is a collaborative project that will enable European SMEs to be more competitive globally through the use of simulation services running on a High Performance Computing cloud infrastructure.
  • CloudFlow: FP7, ICT for Manufacturing SMEs (I4MS) project. Cloudflow will enable the remote use of computational services distributed on the cloud, seamlessly integrating these within established engineering design workflows and standards.

Curriculum Vitae

Publications

2018

  • P. Viviani, M. Aldinucci, R. d’Ippolito, J. Lemeire, and D. Vucinic, “A Flexible Numerical Framework for Engineering—A Response Surface Modelling Application,” in Improved Performance of Materials: Design and Experimental Approaches, A. Öchsner and H. Altenbach, Eds., Cham: Springer International Publishing, 2018, pp. 93-106. doi:10.1007/978-3-319-59590-0_9
    [BibTeX] [Abstract] [Download PDF]

    This work presents an innovative approach adopted for the development of a new numerical software framework for accelerating dense linear algebra calculations and its application within an engineering context. In particular, response surface models (RSM) are a key tool to reduce the computational effort involved in engineering design processes like design optimization. However, RSMs may prove to be too expensive to be computed when the dimensionality of the system and/or the size of the dataset to be synthesized is significantly high or when a large number of different response surfaces has to be calculated in order to improve the overall accuracy (e.g. like when using ensemble modelling techniques). On the other hand, the potential of modern hybrid hardware (e.g. multicore, GPUs) is not exploited by current engineering tools, while they can lead to a significant performance improvement. To fill this gap, a software framework is being developed that enables the hybrid and scalable acceleration of the linear algebra core for engineering applications and especially of RSMs calculations with a user-friendly syntax that allows good portability between different hardware architectures, with no need of specific expertise in parallel programming and accelerator technology. The effectiveness of this framework is shown by comparing an accelerated code to a single-core calculation of a radial basis function RSM on some benchmark datasets. This approach is then validated within a real-life engineering application and the achievements are presented and discussed.

    @inbook{17:viviani:advstruct,
      abstract = {This work presents an innovative approach adopted for the development of a new numerical software framework for accelerating dense linear algebra calculations and its application within an engineering context. In particular, response surface models (RSM) are a key tool to reduce the computational effort involved in engineering design processes like design optimization. However, RSMs may prove to be too expensive to be computed when the dimensionality of the system and/or the size of the dataset to be synthesized is significantly high or when a large number of different response surfaces has to be calculated in order to improve the overall accuracy (e.g. like when using ensemble modelling techniques). On the other hand, the potential of modern hybrid hardware (e.g. multicore, GPUs) is not exploited by current engineering tools, while they can lead to a significant performance improvement. To fill this gap, a software framework is being developed that enables the hybrid and scalable acceleration of the linear algebra core for engineering applications and especially of RSMs calculations with a user-friendly syntax that allows good portability between different hardware architectures, with no need of specific expertise in parallel programming and accelerator technology. The effectiveness of this framework is shown by comparing an accelerated code to a single-core calculation of a radial basis function RSM on some benchmark datasets. This approach is then validated within a real-life engineering application and the achievements are presented and discussed.},
      address = {Cham},
      author = {Viviani, P. and Aldinucci, M. and d'Ippolito, R. and Lemeire, J. and Vucinic, D.},
      booktitle = {Improved Performance of Materials: Design and Experimental Approaches},
      doi = {10.1007/978-3-319-59590-0_9},
      editor = {{\"O}chsner, Andreas and Altenbach, Holm},
      isbn = {978-3-319-59590-0},
      pages = {93--106},
      publisher = {Springer International Publishing},
      title = {A Flexible Numerical Framework for Engineering---A Response Surface Modelling Application},
      url = {https://doi.org/10.1007/978-3-319-59590-0_9},
      year = {2018},
      bdsk-url-1 = {https://doi.org/10.1007/978-3-319-59590-0_9},
      bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-319-59590-0_9}
    }

2017

  • P. Viviani, M. Torquati, M. Aldinucci, and R. d’Ippolito, “Multiple back-end support for the Armadillo linear algebra interface,” in In proc. of the 32nd ACM Symposium on Applied Computing (SAC), Marrakesh, Morocco, 2017, pp. 1566-1573.
    [BibTeX] [Abstract] [Download PDF]

    The Armadillo C++ library provides programmers with a high-level Matlab-like syntax for linear algebra. Its design aims at providing a good balance between speed and ease of use. It can be linked with different back-ends, i.e. different LAPACK-compliant libraries. In this work we present a novel run-time support of Armadillo, which gracefully extends mainstream implementation to enable back-end switching without recompilation and multiple back-end support. The extension is specifically designed to not affect Armadillo class template prototypes, thus to be easily interoperable with future evolutions of the Armadillo library itself. The proposed software stack is then tested for functionality and performance against a kernel code extracted from an industrial application.

    @inproceedings{17:sac:armadillo,
      abstract = {The Armadillo C++ library provides programmers with a high-level Matlab-like syntax for linear algebra. Its design aims at providing a good balance between speed and ease of use. It can be linked with different back-ends, i.e. different LAPACK-compliant libraries. In this work we present a novel run-time support of Armadillo, which gracefully extends mainstream implementation to enable back-end switching without recompilation and multiple back-end support. The extension is specifically designed to not affect Armadillo class template prototypes, thus to be easily interoperable with future evolutions of the Armadillo library itself. The proposed software stack is then tested for functionality and performance against a kernel code extracted from an industrial application.},
      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}
    }

2016

  • P. Viviani, M. Aldinucci, and R. d’Ippolito, “An hybrid linear algebra framework for engineering,” in Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) — Poster Abstracts, Fiuggi, Italy, 2016.
    [BibTeX] [Abstract] [Download PDF]

    The aim of this work is to provide developers and domain experts with simple (Matlab-like) inter- face for performing linear algebra tasks while retaining state-of-the-art computational speed. To achieve this goal we extend Armadillo C++ library is extended in order to support with multiple LAPACK-compliant back-ends targeting different architectures including CUDA GPUs; moreover our approach involves the possibility of dynamically switching between such back-ends in order to select the one which is most convenient based on the specific problem and hardware configura- tion. This approach is eventually validated within an industrial environment.

    @inproceedings{16:acaces:armadillo,
      abstract = {The aim of this work is to provide developers and domain experts with simple (Matlab-like) inter- face for performing linear algebra tasks while retaining state-of-the-art computational speed. To achieve this goal we extend Armadillo C++ library is extended in order to support with multiple LAPACK-compliant back-ends targeting different architectures including CUDA GPUs; moreover our approach involves the possibility of dynamically switching between such back-ends in order to select the one which is most convenient based on the specific problem and hardware configura- tion. This approach is eventually validated within an industrial environment.},
      address = {Fiuggi, Italy},
      author = {Paolo Viviani and Marco Aldinucci and Roberto d'Ippolito},
      booktitle = {Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) -- Poster Abstracts},
      date-added = {2016-08-20 17:22:51 +0000},
      date-modified = {2016-08-20 17:29:35 +0000},
      keywords = {nvidia,algebra, gpu, itea2, repara},
      month = {July},
      title = {An hybrid linear algebra framework for engineering},
      url = {https://iris.unito.it/retrieve/handle/2318/1622382/300198/armadillo.pdf},
      year = {2016},
      bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1622382/300198/armadillo.pdf}
    }

  • P. Viviani, M. Aldinucci, R. d’Ippolito, J. Lemeire, and D. Vucinic, “A flexible numerical framework for engineering – a Response Surface Modelling application,” in 10th Intl. Conference on Advanced Computational Engineering and Experimenting (ACE-X), 2016.
    [BibTeX] [Abstract]

    This work presents the innovative approach adopted for the development of a new numerical software framework for accelerating Dense Linear Algebra calculations and its application within an engineering context. In particular, Response Surface Models (RSM) are a key tool to reduce the computational effort involved in engineering design processes like design optimization. However, RSMs may prove to be too expensive to be computed when the dimensionality of the system and/or the size of the dataset to be synthesized is significantly high or when a large number of different Response Surfaces has to be calculated in order to improve the overall accuracy (e.g. like when using Ensemble Modelling techniques). On the other hand, it is a known challenge that the potential of modern hybrid hardware (e.g. multicore, GPUs) is not exploited by current engineering tools, while they can lead to a significant performance improvement. To fill this gap, a software framework is being developed that enables the hybrid and scalable acceleration of the linear algebra core for engineering applications and especially of RSMs calculations with a user-friendly syntax that allows good portability between different hardware architectures, with no need of specific expertise in parallel programming and accelerator technology. The effectiveness of this framework is shown by comparing an accelerated code to a single-core calculation of a Radial Basis Function RSM on some benchmark datasets. This approach is then validated within a real-life engineering application and the achievements are presented and discussed.

    @inproceedings{16:acex:armadillo,
      abstract = {This work presents the innovative approach adopted for the development of a new numerical software framework for accelerating Dense Linear Algebra calculations and its application within an engineering context.
    In particular, Response Surface Models (RSM) are a key tool to reduce the computational effort involved in engineering design processes like design optimization. However, RSMs may prove to be too expensive to be computed when the dimensionality of the system and/or the size of the dataset to be synthesized is significantly high or when a large number of different Response Surfaces has to be calculated in order to improve the overall accuracy (e.g. like when using Ensemble Modelling techniques).
    On the other hand, it is a known challenge that the potential of modern hybrid hardware (e.g. multicore, GPUs) is not exploited by current engineering tools, while they can lead to a significant performance improvement. To fill this gap, a software framework is being developed that enables the hybrid and scalable acceleration of the linear algebra core for engineering applications and especially of RSMs calculations with a user-friendly syntax that allows good portability between different hardware architectures, with no need of specific expertise in parallel programming and accelerator technology.
    The effectiveness of this framework is shown by comparing an accelerated code to a single-core calculation of a Radial Basis Function RSM on some benchmark datasets. This approach is then validated within a real-life engineering application and the achievements are presented and discussed.
    },
      author = {Paolo Viviani and Marco Aldinucci and Roberto d'Ippolito and Jean Lemeire and Dean Vucinic},
      booktitle = {10th Intl. Conference on Advanced Computational Engineering and Experimenting (ACE-X)},
      date-added = {2016-08-19 21:37:19 +0000},
      date-modified = {2017-06-19 15:35:39 +0000},
      keywords = {repara, rephrase, nvidia, gpu},
      title = {A flexible numerical framework for engineering - a Response Surface Modelling application},
      year = {2016}
    }

2015

  • P. Viviani, “Parallel Computing Techniques for High Energy Physics,” Master Thesis, 2015.
    [BibTeX] [Abstract]

    Modern experimental achievements, with LHC results as a prominent but not exclusive representative, have undisclosed a new range of challenges concerning theoretical com- putations. Tree level QED calculation are no more satisfactory due to the very small experimental uncertainty of precision e+ e- measurements, so Next To Leading and Next to Next to Leading Order calculations are required. At the same time many-legs, high-order QCD processes needed to simulate LHC events are raising even more the bar of computational complexity. The drive for the present work has been the interest in calculating high multiplicity Higgs boson processes with a dedicated software library (RECOLA) currently under development at the University of Torino, as well as the related technological challenges. This thesis undertakes the task of exploring the possibilities offered by present and upcoming computing technologies in order to face these challenges properly. The first two chapters outlines the theoretical context and the available technologies. In chapter 3 a a case study is examined in full detail, in order to explore the suitability of different parallel computing solutions. In the chapter 4, some of those solutions are implemented in the context of the RECOLA library, allowing it to handle processes at a previously unexplored scale of complexity. Alongside, the potential of new, cost-effective parallel architectures is tested.

    @mastersthesis{tesi:viviani:15,
      abstract = { Modern experimental achievements, with LHC results as a prominent but not exclusive representative, have undisclosed a new range of challenges concerning theoretical com- putations. Tree level QED calculation are no more satisfactory due to the very small experimental uncertainty of precision e+ e- measurements, so Next To Leading and Next to Next to Leading Order calculations are required. At the same time many-legs, high-order QCD processes needed to simulate LHC events are raising even more the bar of computational complexity. The drive for the present work has been the interest in calculating high multiplicity Higgs boson processes with a dedicated software library (RECOLA) currently under development at the University of Torino, as well as the related technological challenges.
    This thesis undertakes the task of exploring the possibilities offered by present and upcoming computing technologies in order to face these challenges properly. The first two chapters outlines the theoretical context and the available technologies. In chapter 3 a a case study is examined in full detail, in order to explore the suitability of different parallel computing solutions. In the chapter 4, some of those solutions are implemented in the context of the RECOLA library, allowing it to handle processes at a previously unexplored scale of complexity. Alongside, the potential of new, cost-effective parallel architectures is tested.},
      author = {Paolo Viviani},
      date-added = {2015-09-27 12:36:54 +0000},
      date-modified = {2015-09-27 13:28:24 +0000},
      keywords = {fastflow,impact},
      school = {Physics Department, University of Torino},
      title = {Parallel Computing Techniques for High Energy Physics},
      year = {2015}
    }