publications

2026

1. Lagrange: Operating Italy’s First Publicly-Accessible Quantum Computer for Research and Education

   Paolo Viviani, Fabrizio Bertone, Giacomo Vitali, and 14 more authors

   2026

   arXiv
2. Three ways to share a QPU: Scheduling strategies for hybrid Quantum-HPC applications

   Marco Cipollini, Simone Rizzo, Sergio Iserte, and 21 more authors

   Future Generation Computer Systems, 2026

   Abs DOI

   As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources. To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimise classical resource utilisation, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilisation and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterised by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.

3. Digital Quantum Reservoir Computing for ATM Time Series Prediction

   Chiara Vercellino, Giacomo Vitali, Valeria Zaffaroni, and 5 more authors

   2026

   arXiv
4. Quantum-Assisted Design of Space-Terrestrial Integrated Networks

   Chiara Vercellino, Giacomo Vitali, Paolo Viviani, and 5 more authors

   2026

   arXiv
5. Standard candle based distance estimation with learning algorithms

   Virginia Ajani, Martina Giovalli, Paolo Viviani, and 6 more authors

   Astronomy and Computing, 2026

   Abs DOI

   Measuring distances to celestial objects, such as stars and galaxies, is essential to characterizing their physical properties, formation, and evolution, and provides fundamental constraints on the expansion rate of the Universe. In this work, we present a methodological study comparing several machine learning and deep learning approaches for predicting astrophysical parameters — such as parallax, astrometry-based luminosity, and distance — using Cepheids and RR Lyrae samples from Gaia DR3 catalogues as input. In parallel, we introduce a framework to exploit the historical archive of photographic plates from INAF-OATo. In this context we extract a catalogue of objects from the plates, then cross-match the output sources with the Gaia dataset, with the goal of extending light curves that could serve as additional input for the models. Preliminary results identify the Gaussian Process regressor as the best-performing model among those tested, and the Multi-Layer Perceptron (MLP) as the most promising deep learning approach. We further study the propagation of uncertainties, enabling us to incorporate them both into the models and the predictions. For the plate analysis, we chose an image of the LMC field with 43 cepheids in common with the Gaia catalogue as a first case study to validate our methodology.

2025

1. Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation

   Roberto Rocco, Simone Rizzo, Matteo Barbieri, and 16 more authors

   In 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 2025

   DOI
2. Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform

   Giacomo Vitali, Chiara Vercellino, Paolo Viviani, and 9 more authors

   2025

   arXiv
3. Hybrid Quantum-Classical Branch-and-Price Method for the Vertex Coloring Problem

   Chiara Vercellino, M. Yassine Naghmouchi, Wesley Coelho, and 5 more authors

   2025

   arXiv
4. Assessing the Elephant in the Room in Scheduling for Current Hybrid HPC-QC Clusters

   Paolo Viviani, Roberto Rocco, Matteo Barbieri, and 14 more authors

   In 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2025

   DOI

2024

1. Harnessing a 256-Qubit Neutral Atom Simulator for Graph Classification

   Edoardo Giusto, Gabriele Iurlaro, Bartolomeo Montrucchio, and 5 more authors

   In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Sep 2024

   Abs DOI

   Neutral atom platforms are analogue quantum simulators that offer the possibility to map graphs onto a 2D qubit register using programmable Rubidium atoms arrays, whose valence electrons’ energy state is used as qubits, using optical tweezers. This makes it possible to implement algorithms for solving graph combinatorial optimization and Quantum Machine Learning (QML) tasks, such as graph classification. However, the restrictions of real hardware, as well as the very low number of publicly available machines, make such implementation nontrivial. In this work, we manage to compute the Quantum Evolution Kernel (QEK) to extract the features from graphs of the PROTEINS dataset using the 256-qubits Aquila platform (available through AWS) and then we apply classical Machine Learning (ML) techniques for the final classification. The method is benchmarked against classical kernels, resulting in slightly better performance, proving the effectiveness of the method, even in the case of a noisy quantum simulator.

2. Harnessing DEN Models for Quantum Computing Tasks on Neutral Atom QPUs

   Chiara Vercellino, Giacomo Vitali, Paolo Viviani, and 3 more authors

   In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Sep 2024

   Abs DOI

   We present our work on effectively representing unit-disk graphs on the registers of neutral atom quantum machines. Specifically, we aimed to embed graphs corresponding to proteins and cellular antenna networks into unit-disk graphs, ensuring compatibility with the registers of two real QPUs: Orion Alpha by PASQAL and Aquila by QuEra. To address machine-specific constraints, we made adjustments and integrated Distance Encoder Networks (DEN) from our previous work. Despite these challenges, we successfully embedded up to 76% of protein-representing graphs for a quantum machine learning classification task on the Aquila QPU, and all subgraphs derived from 90 antenna geographical positions in Turin, Italy, on the Orion Alpha QPU. In the latter case, the graphs represented instances of the graph coloring problem, which we tackled using the hybrid quantum-classical algorithm BBQ-mIS. These promising results underscore the effectiveness and versatility of our embedding approach for representing unit-disk graphs on neutral atom quantum computers across diverse applications.

3. A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations

   Daniele Biassoni, Matteo Russo, Paolo Viviani, and 2 more authors

   Jun 2024

   Abs DOI

   The subject of the present paper is the development of a general procedure to calculate the terms of the total pressure transport equations using a High-Performance Data Analytics (HPDA), based on Proper Orthogonal Decomposition (POD) and leveraging on High Performance Computing. This method is applied to data obtained from high fidelity simulations of low pressure turbine (LPT) blades in order to separate the different loss contributions and to visualize the associated structures to the fluid dynamics phenomena that occur inside the passage. This procedure is developed in Python environment because it easily allows parallel computing.This paper discusses the mathematical framework behind the decomposition of total pressure transport equation and its implementation in Python. The scalability tests are performed for two exemplary datasets and compared to previous implementation showing a considerable speed-up. The procedure applied to Large Eddy Simulation (LES) data shows significant improvement over traditional approaches. It provides more detailed information about the phenomena associated with the generation of losses in the turbine blades, allowing for quick identification of where these losses occur. The HPDA code can be applied to all high fidelity simulations (LES and DNS) in order to get more information from simulations that are extremely expensive, allowing the full exploitation of such large datasets. In addition, to demonstrate the ease of code’s implementation, the data obtained from the POD are compared with data obtained from Fourier decomposition to validate the procedure. The procedure is open access and available in an online repository.

4. Demistifying HPC-Quantum integration: it’s all about scheduling

   Paolo Viviani

   In Proceedings of the 2024 Workshop on High Performance and Quantum Computing Integration, Pisa, Italy, 2024

   Abs DOI

   Recent research on the integration between HPC and quantum computer was mostly focused on the software stack and quantum circuit compilation aspects, neglecting critical issues like HPC resource allocation and job scheduling given the scarcity of QPUs, and disregarding the heterogeneity of current quantum technologies and their computational models (e.g., digital vs. analogue). This work would like to bring the attention to issues that are critical to achieve integration with operational HPC environments given the current status of quantum computers maturity and heterogeneity.

5. Advanced Resource Allocation in the Context of Heterogeneous Workflows Management

   Francesco Lubrano, Chiara Vercellino, Giacomo Vitali, and 3 more authors

   In Proceedings of the 2nd Workshop on Workflows in Distributed Environments, Apr 2024

   Abs DOI

   In High-Performance Computing (HPC), workflows are utilized to define and manage a set of interdependent computations which allow the users to extract insights from (scientific) numerical simulations or data analytics. HPC platforms can perform extreme-scale simulations, combining Artificial Intelligence (AI) training and inference and data analytics (we refer to heterogeneous workflows), by providing tools and computing resources which serve a variety of use-cases spanning very diverse application domains (e.g., weather forecasting, quantum mechanics, etc.). Executing such workflows at scale requires to handle dependencies, job submission automation, I/O mechanisms. Despite State-of-the-Art batch schedulers can be configured and integrated with tools accomplishing this automation, a number of cases where resource allocation can lead to inefficiencies still exist. In this paper, to overcome these limitations, we present the WARP (Workflow-aware Advanced Resource Planner), a tool that integrates with workflow management tools and batch schedulers, to reserve in advance resources for an optimal execution of jobs, based on their duration, dependencies and machine load. WARP has been designed to minimize the overall workflow execution, without violating the priority policies for cluster users imposed by the system administrators.

2023

1. BBQ-mIS: A Parallel Quantum Algorithm for Graph Coloring Problems

   Chiara Vercellino, Giacomo Vitali, Paolo Viviani, and 5 more authors

   In 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Sep 2023

   Abs DOI

   Among the limitations of current quantum machines, the qubits count represents one of the most critical challenges for porting reasonably large computational problems, such as those coming from real-world applications, to the scale of the quantum hardware. In this regard, one possibility is to decompose the problems at hand and exploit parallelism over multiple size-limited quantum resources. To this purpose, we designed a hybrid quantum-classical algorithm, i.e., BBQ-mIS, to solve graph coloring problems on Rydberg atoms quantum machines. The BBQ-mIS algorithm combines the natural representation of Maximum Independent Set (MIS) problems onto the machine Hamiltonian with a Branch&Bound (BB) approach to identify a proper graph coloring. In the proposed solution, the graph representation emerges from qubit interactions (qubits represent vertexes of the graph), and the coloring is then retrieved by iteratively assigning one color to a maximal set of independent vertexes of the graph, still minimizing the number of colors with the Branch&Bound approach. We emulated real quantum hardware onto an IBM Power9-based cluster, with 32 cores/node and 256 GB/node, and exploited an MPI-enhanced library to implement the parallelism for the BBQ-mIS algorithm. Considering this use case, we also identify some technical requirements and challenges for an effective HPC-QC integration. The results show that our problem decomposition is effective in terms of graph coloring solutions quality, and provide a reference for applying this methodology to other quantum technologies or applications.

2. Deep Learning for Real-Time Neural Decoding of Grasp

   Paolo Viviani, Ilaria Gesmundo, Elios Ghinato, and 9 more authors

   In Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, 2023

   Abs DOI

   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.

3. Neural Optimization for Quantum Architectures: Graph Embedding Problems with Distance Encoder Networks

   Chiara Vercellino, Giacomo Vitali, Paolo Viviani, and 5 more authors

   In 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Jun 2023

   Abs DOI

   Quantum machines are among the most promising technologies expected to provide significant improvements in the following years. However, bridging the gap between real-world applications and their implementation on quantum hardware is still a complicated task. One of the main challenges is to represent through qubits (i.e., the basic units of quantum information) the problems of interest. According to the specific technology under-lying the quantum machine, it is necessary to implement a proper representation strategy, generally referred to as embedding. This paper introduces a neural-enhanced optimization framework to solve the constrained unit disk problem, which arises in the context of qubits positioning for neutral atoms-based quantum hardware. The proposed approach involves a modified autoencoder model, i.e., the Distances Encoder Network, and a custom loss, i.e., the Embedding Loss Function, respectively, to compute Euclidean distances and model the optimization constraints. The core idea behind this design relies on the capability of neural networks to approximate non-linear transformations to make the Distances Encoder Network learn the spatial transformation that maps initial non-feasible solutions of the constrained unit disk problem into feasible ones. The proposed approach outperforms classical solvers, given fixed comparable computation times, and paves the way to address other optimization problems through a similar strategy.

4. A Machine Learning Approach for an HPC Use Case: the Jobs Queuing Time Prediction

   Chiara Vercellino, Alberto Scionti, Giuseppe Varavallo, and 3 more authors

   Future Generation Computer Systems, 2023

   Abs DOI

   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.

2022

1. Accelerating Legacy Applications with Spatial Computing Devices

   Paolo Savio, Alberto Scionti, Giacomo Vitali, and 8 more authors

   The Journal of Supercomputing, Nov 2022

   Abs DOI

   Abstract Heterogeneous computing is the major driving factor in designing new energy-efficient high-performance computing systems. Despite the broad adoption of GPUs and other specialized architectures, the interest in spatial architectures like field-programmable gate arrays (FPGAs) has grown. While combining high performance, low power consumption and high adaptability constitute an advantage, these devices still suffer from a weak software ecosystem, which forces application developers to use tools requiring deep knowledge of the underlying system, often leaving legacy code (e.g., Fortran applications) unsupported. By realizing this, we describe a methodology for porting Fortran (legacy) code on modern FPGA architectures, with the target of preserving performance/power ratios. Aimed as an experience report, we considered an industrial computational fluid dynamics application to demonstrate that our methodology produces synthesizable OpenCL codes targeting Intel Arria10 and Stratix10 devices. Although performance gain is not far beyond that of the original CPU code (we obtained a relative speedup of \\\times\ \texttimes  0.59 and \\\times\ \texttimes  0.63, respectively, for a single optimized main kernel, while only on the Stratix10 we achieved \\\times\ \texttimes  2.56 by replicating the main optimized kernel 4 times), our results are quite encouraging to drawn the path for further investigations. This paper also reports some major criticalities in porting Fortran code on FPGA architectures.

2. Neural-Powered Unit Disk Graph Embedding: Qubits Connectivity for Some QUBO Problems

   Chiara Vercellino, Paolo Viviani, Giacomo Vitali, and 5 more authors

   In 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Sep 2022

   Abs DOI

   Graph embedding is a recurrent problem in quantum computing, for instance, quantum annealers need to solve a minor graph embedding in order to map a given Quadratic Unconstrained Binary Optimization (QUBO) problem onto their internal connectivity pattern. This work presents a novel approach to constrained unit disk graph embedding, which is encountered when trying to solve combinatorial optimization problems in QUBO form, using quantum hardware based on neutral Rydberg atoms. The qubits, physically represented by the atoms, are excited to the Rydberg state through laser pulses. Whenever qubits pairs are closer together than the blockade radius, entanglement can be reached, thus preventing entangled qubits to be simultaneously in the excited state. Hence, the blockade radius determines the adjacency pattern among qubits, corresponding to a unit disk configuration. Although it is straightforward to compute the adjacency pattern given the qubits’ coordinates, identifying a feasible unit disk arrangement that matches the desired QUBO matrix is, on the other hand, a much harder task. In the context of quantum optimization, this issue translates into the physical placement of the qubits in the 2D/3D register to match the machine’s Ising-like Hamiltonian with the QUBO formulation of the optimization problems. The proposed solution exploits the power of neural networks to transform an initial embedding configuration, which does not match the quantum hardware requirements or does not account for the unit disk property, into a feasible embedding properly representing the target optimization problems. Experimental results show that this new approach overcomes in performance Gurobi solver.

3. Taming Multi-node Accelerated Analytics: An Experience in Porting MATLAB to Scale with Python

   Paolo Viviani, Giacomo Vitali, Davide Lengani, and 3 more authors

   In Complex, Intelligent and Software Intensive Systems, 2022

   Series Title: Lecture Notes in Networks and Systems

   DOI
4. Dynamic Job Allocation on Federated Cloud-HPC Environments

   Giacomo Vitali, Alberto Scionti, Paolo Viviani, and 2 more authors

   In Complex, Intelligent and Software Intensive Systems, 2022

   Series Title: Lecture Notes in Networks and Systems

   DOI
5. Distributed HPC Resources Orchestration for Supporting Large-Scale Workflow Execution

   Alberto Scionti, Paolo Viviani, Giacomo Vitali, and 4 more authors

   In HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, Jan 2022

   Abs DOI

   Artificial intelligence (AI) is gaining momentum in the scientific and industrial community for the ever-growing number of applications where such innovative techniques of learning form and processing large amount of data have proved successful. High-performance computing (HPC) and cloud resources providers are moving faster to be able to support new applications that benefit from the combination of traditional HPC simulation, machine learning and deep learning processing and big data analytics. However, the tighter the combination of these three elements is, the more complex the integration of innovative architectures into a single execution platform becomes. On one hand, application workflow management systems need to incorporate more functionalities and support dynamism in the execution, by preserving (energy) efficiency of the infrastructural resources. On the other hand, more exotic hardware accelerators (ranging from GPUs and FPGAs, to neural network processors (NNPs), to neuromorphic processors) need to be integrated in the computing assets in order to leverage performance boost. This chapter provides an overview of the future HPC, AI, and big-data cross-stack execution platform, as devised in the funded EuroHPC ACROSS project, which will be tailored to cope with all these challenges, and to support future exascale-ready applications.

6. Enabling the HPC and Artificial Intelligence Cross-Stack Convergence at the Exascale Level

   Alberto Scionti, Paolo Viviani, Giacomo Vitali, and 2 more authors

   In HPC, Big Data, and AI Convergence Towards Exascale: Challenge and Vision, Jan 2022

   Abs DOI

   Artificial intelligence (AI) is gaining momentum in the scientific and industrial community for the ever-growing number of applications where such innovative techniques of learning form and processing large amount of data have proved successful. High-performance computing (HPC) and cloud resources providers are moving faster to be able to support new applications that benefit from the combination of traditional HPC simulation, machine learning and deep learning processing and big data analytics. However, the tighter the combination of these three elements is, the more complex the integration of innovative architectures into a single execution platform becomes. On one hand, application workflow management systems need to incorporate more functionalities and support dynamism in the execution, by preserving (energy) efficiency of the infrastructural resources. On the other hand, more exotic hardware accelerators (ranging from GPUs and FPGAs, to neural network processors (NNPs), to neuromorphic processors) need to be integrated in the computing assets in order to leverage performance boost. This chapter provides an overview of the future HPC, AI, and big-data cross-stack execution platform, as devised in the funded EuroHPC ACROSS project, which will be tailored to cope with all these challenges, and to support future exascale-ready applications.

2021

1. HPC-Cloud-Big Data Convergent Architectures and Research Data Management: The LEXIS Approach

   Stephan Hachinger, Jan Martinovič, Olivier Terzo, and 21 more authors

   In Proceedings of International Symposium on Grids & Clouds 2021 — PoS(ISGC2021), 2021

   Abs DOI

   The LEXIS project (Large-scale EXecution for Industry & Society, H2020 GA825532) provides a platform for optimised execution of Cloud-HPC workflows, reducing computation time and increasing energy efficiency. The system will rely on advanced, distributed orchestration solutions (Atos YSTIA Suite, with Alien4Cloud and Yorc, based on TOSCA), the High-End Application Execution Middleware HEAppE, and new hardware capabilities for maximising efficiency in data processing, analysis and transfer (e.g. Burst Buffers with GPU- and FPGA-based data reprocessing). LEXIS handles computation tasks and data from three Pilots, based on representative and demanding HPC/Cloud-Computing use cases in Industry (SMEs) and Science: i) Simulations of complex turbomachinery and gearbox systems in Aeronautics, ii) Tsunami simulations and earthquake loss assessments which are time-constrained to enable immediate warnings and to support well-informed decisions, and iii) Weather and Climate simulations where massive amounts of in-situ data are assimilated to improve forecasts. A user-friendly LEXIS web portal, as a unique entry point, will provide access to data as well as workflow-handling and remote visualisation functionality. As part of its back-end, LEXIS builds an elaborate system for the handling of input, intermediate and result data. At its core, a Distributed Data Infrastructure (DDI) ensures the availability of LEXIS data at all participating HPC sites, which will be federated with a common LEXIS Authentication and Authorisation Infrastructure (with unified security model, user database and policies). The DDI leverages best of breed data-management solutions from EUDAT, such as B2SAFE (based on iRODS) and B2HANDLE. REST APIs on top of it will ensure a smooth interaction with LEXIS workflows and the orchestration layer. Last, but not least, the DDI will provide functionalities for Research Data Management following the FAIR principles (“Findable, Interoperable, Accessible, Reusable”), e.g. DOI acquisition, which helps to publish and disseminate open data products.

2. Towards Optimal Graph Coloring Using Rydberg Atoms

   Giacomo Vitali, Paolo Viviani, Chiara Vercellino, and 5 more authors

   In The International Conference for High Performance Computing, Networking, Storage, and Analysis, Research posters, St. Louis, MO, USA, 2021

   Abs HTML

   Quantum mechanics is expected to revolutionize the computing landscape in the near future. Among the many candidate technologies for building universal quantum computers, Rydberg atoms-based systems stand out for being capable of performing both quantum simulations and working as gate-based universal quantum computers while operating at room temperature through an optical system. Moreover, they can potentially scale up to hundreds of quantum bits (qubits). In this work, we solve a Graph Coloring problem by iteratively computing the solutions of Maximal Independent Set (MIS) problems, exploiting the Rydberg blockade phenomenon. Experimental results using a simulation framework on the CINECA Marconi-100 supercomputer demonstrate the validity of the proposed approach.

2020

1. Authenticated and Auditable Data Sharing via Smart Contract

   Vincent Reniers, Yuan Gao, Ren Zhang, and 8 more authors

   In Proceedings of the 35th Annual ACM Symposium on Applied Computing, Brno, Czech Republic, 2020

   Abs DOI

   Our main use case features multiple companies that iteratively optimize on the architectural properties of aircraft components in a decentralized manner. In each optimization step of the so-called multi-disciplinary optimization (MDO) process, sensitive data is exchanged between organizations, and we require auditability and traceability of actions taken to assure compliance with signed legal agreements.In this paper, we present a distributed protocol that coordinates authenticated and auditable exchanges of files, leveraging a smart contract. The entire life cycle of a file exchange, including file registration, access request and key distribution, is recorded and traceable via the smart contract. Moreover, when one party raises a dispute, the smart contract can be used to identify the dishonest party without compromising the file’s confidentiality.The proposed protocol provides a simple, novel, yet efficient approach to exchange files with support for data access auditability between companies involved in a private consortium with no incentive to share files outside of the protocol. We implemented the protocol in Solidity, deployed it on a private Ethereum blockchain, and validated it within the use case of a decentralized workflow.

2019

1. Deep Learning at Scale with Nearest Neighbours Communications

   Paolo Viviani

   Computer Science Department, University of Torino, Sep 2019

   Abs DOI

   As deep learning techniques become more and more popular, there is the need to move these applications from the data scientist’s Jupyter notebook to efficient and reliable enterprise solutions. Moreover, distributed training of deep learning models will happen more and more outside the well-known borders of cloud and HPC infrastructure and will move to edge and mobile platforms. Current techniques for distributed deep learning have drawbacks in both these scenarios, limiting their long-term applicability. After a critical review of the established techniques for Data Parallel training from both a distributed computing and deep learning perspective, a novel approach based on nearest-neighbour communications is presented in order to overcome some of the issues related to mainstream approaches, such as global communication patterns. Moreover, in order to validate the proposed strategy, the Flexible Asynchronous Scalable Training (FAST) framework is introduced, which allows to apply the nearest-neighbours communications approach to a deep learning framework of choice. Finally, a relevant use-case is deployed on a medium-scale infrastructure to demonstrate both the framework and the methodology presented. Training convergence and scalability results are presented and discussed in comparison to a baseline defined by using state-of-the-art distributed training tools provided by a well-known deep learning framework.

2. Analysis of Architectural Variants for Auditable Blockchain-based Private Data Sharing

   Vincent Reniers, Dimitri Van Landuyt, Paolo Viviani, and 3 more authors

   In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, Limassol, Cyprus, 2019

   Abs DOI

   Many applications by design depend on costly trusted third-party auditors. One such example is the industrial application case of federated multi-disciplinary optimization (MDO), in which different organizations contribute to a complex engineering design effort. Although blockchain and distributed ledger technology (DLT) has strong potential in reducing the dependence on such intermediaries, the architectural complexity involved in designing a solution is daunting. In this paper, we analyze the architectural variants for decentralized private data sharing while guaranteeing auditability in terms of data access operations. Non-repudiation of actions taken by each party is a key requirement, as is availability of the shared data. % through storage governed by the chain. The architectural variants analyzed focus on attaining: (i) confidential data exchange, (ii) maintaining and governing access to the shared data, (iii) providing data access auditability, (iv) data validation or conflict resolution, and to a lesser degree (v) transaction and identity privacy. We systematically enumerate architectural decisions at the levels of: storage, policy-based file access control, data encryption methods, and auditability mechanisms for private data. This analysis is based on extensive assessment of the state of the art on decentralized private data access management using static or dynamic policies, and private data validation without exposing confidential information. The main contribution of this work is a comprehensive overview of architectural variants for decentralized control of private, encrypted data, and the involved trade-offs in terms of performance, auditable trust and security. These findings are validated in the context on the aforementioned industry case that involves federated multi-disciplinary optimization (MDO).

3. Accelerating spectral graph analysis through wavefronts of linear algebra operations

   Maurizio Drocco, Paolo Viviani, Iacopo Colonnelli, and 2 more authors

   In Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), 2019

   Abs DOI

   The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.

4. Deep Learning at Scale

   Paolo Viviani, Maurizio Drocco, Daniele Baccega, and 2 more authors

   In Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), 2019

   Abs DOI

   This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.

2018

1. Pushing the boundaries of parallel Deep Learning - A practical approach

   Paolo Viviani, Maurizio Drocco, and Marco Aldinucci

   CoRR, 2018

   arXiv
2. HPC4AI, an AI-on-demand federated platform endeavour

   Marco Aldinucci, Sergio Rabellino, Marco Pironti, and 18 more authors

   In ACM Computing Frontiers, May 2018

   Abs DOI

   In April 2018, under the auspices of the POR-FESR 2014-2020 program of Italian Piedmont Region, the Turin’s Centre on High-Performance Computing for Artificial Intelligence (HPC4AI) was funded with a capital investment of 4.5Me and it began its deployment. HPC4AI aims to facilitate scientific research and engineering in the areas of Artificial Intelligence and Big Data Analytics. HPC4AI will specifically focus on methods for the on-demand provisioning of AI and BDA Cloud services to the regional and national industrial community, which includes the large regional ecosystem of Small-Medium Enterprises (SMEs) active in many different sectors such as automotive, aerospace, mechatronics, manufacturing, health and agrifood.

3. Scaling Dense Linear Algebra on Multicore and Beyond: a Survey

   Paolo Viviani, Maurizio Drocco, and Marco Aldinucci

   In Proc. of 26th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), 2018

   Abs DOI

   The present trend in big-data analytics is to exploit algorithms with (sub-)linear time complexity, in this sense it is usually worth to investigate if the available techniques can be approximated to reach an affordable complexity. However, there are still problems in data science and engineering that involve algorithms with higher time complexity, like matrix inversion or Singular Value Decomposition (SVD). This work presents the results of a survey that reviews a number of tools meant to perform dense linear algebra at “Big Data” scale: namely, the proposed approach aims first to define a feasibility boundary for the problem size of shared-memory matrix factorizations, then to understand whether it is convenient to employ specific tools meant to scale out such dense linear algebra tasks on distributed platforms. The survey will eventually discuss the presented tools from the point of view of domain experts (data scientist, engineers), hence focusing on the trade-off between usability and performance.

4. Scientific Workflows on Clouds with Heterogeneous and Preemptible Instances

   Fabio Tordini, Marco Aldinucci, Paolo Viviani, and 2 more authors

   In Proc. of the Intl. Conference on Parallel Computing, ParCo 2017, 12-15 September 2017, Bologna, Italy, 2018

   Abs DOI

   The cloud environment is increasingly appealing for the HPC community, which has always dealt with scientific applications. However, there is still some skepticism about moving from traditional physical infrastructures to virtual HPC clusters. This mistrusting probably originates from some well known factors, including the effective economy of using cloud services, data and software availability, and the longstanding matter of data stewardship. In this work we discuss the design of a framework (based on Mesos) aimed at achieving a cost-effective and efficient usage of heterogeneous Processing Elements (PEs) for workflow execution, which supports hybrid cloud bursting over preemptible cloud Virtual Machines.

5. A Flexible Numerical Framework for Engineering—A Response Surface Modelling Application

   P. Viviani, M. Aldinucci, R. d’Ippolito, and 2 more authors

   2018

   Abs DOI

   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.

2017

1. Multiple Back-End Support for the Armadillo Linear Algebra Interface

   Paolo Viviani, Marco Aldinucci, Massimo Torquati, and 1 more author

   In Proceedings of the Symposium on Applied Computing, Marrakech, Morocco, 2017

   Abs DOI

   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.

2016

1. An hybrid linear algebra framework for engineering

   Paolo Viviani, Marco Aldinucci, and Roberto d’Ippolito

   In Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) – Poster Abstracts, Jul 2016

   Abs HTML

   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.

2. A flexible numerical framework for engineering - a Response Surface Modelling application

   Paolo Viviani, Marco Aldinucci, Roberto d’Ippolito, and 2 more authors

   In 10th Intl. Conference on Advanced Computational Engineering and Experimenting (ACE-X), 2016

   Abs

   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.

2015

1. Parallel Computing Techniques for High Energy Physics

   Paolo Viviani

   Physics Department, University of Torino, 2015

   Abs

   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.