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Carlo Cavazzoni

Publications and source records attributed to Carlo Cavazzoni.

15 recordsLinked to original sources
arXiv2025

Investigation of particle dynamics and classification mechanism in a spiral jet mill through computational fluid dynamics and discrete element methods

Predicting the outcome of jet-milling based on the knowledge of process parameters and starting material properties is a task still far from being accomplished. Given the technical difficulties in measuring thermodynamics, flow properties and particle statistics directly in the mills, modelling and simulations constitute alternative tools to gain insight in the process physics and many papers have been recently published on the subject. An ideal predictive simulation tool should combine the correct description of non-isothermal, compressible, high Mach number fluid flow, the correct particle-fluid and particle-particle interactions and the correct fracture mechanics of particle upon collisions but it is not currently available. In this paper we present our coupled CFD-DEM simulation results; while comparing them with the recent modelling and experimental works we will review the current understating of the jet-mill physics and particle classification. Subsequently we analyze the missing elements and the bottlenecks currently limiting the simulation technique as well as the possible ways to circumvent them towards a quantitative, predictive simulation of jet-milling.

physics.comp-ph↗
arXiv2025

Optical properties of emeraldine salt polymers from ab initio calculations: comparison with recent experimental data

We present absorption coefficient α(ω), transverse dielectric function ε(ω), optical conductivity σ(ω), and reflectance R(ω) calculated for an emeraldine salt conducting polymer in its crystalline three-dimensional polaronic structure. We utilize Kohn-Sham DFT electronic wavefunctions and energies implemented in the expression of the macroscopic transverse dielectric function in the framework of the band theory without the electron-hole interaction. Contributions of intra-band transitions are taken into account by adding a Drude-like term to the dielectric function calculated ab-initio. Comparison with optical properties, recently measured on high-quality emeraldine salts (Nature 441(2006)65-68), and with optical absorption spectra, recorded on other emeraldine salts, is very satisfactory. The calculated spectra are discussed in terms of energy-band structure, density of states, inter- and intra-band transitions and transverse dielectric function.

cond-mat.mtrl-sci↗
arXiv2024

Evaluating the Practicality of Quantum Optimization Algorithms for Prototypical Industrial Applications

The optimization of the power consumption of antenna networks is a problem with a potential impact in the field of telecommunications. In this work, we investigate the application of the quantum approximate optimization algorithm (QAOA) and the quantum adiabatic algorithm (QAA), to the solution of a prototypical model in this field. We use statevector emulation in a high-performance computing environment to compare the performance of these two algorithms in terms of solution quality, using selected evaluation metrics. We estimate the circuit depth scaling with the problem size while maintaining a certain level of solution quality, and we extend our analysis up to 31 qubits, which is rarely addressed in the literature. Our calculations show that as the problem size increases, the probability of measuring the exact solution decreases exponentially for both algorithms. This issue is particularly severe when we include constraints in the problem, resulting in full connectivity between the sites. Nonetheless, we observe that the cumulative probability of measuring solutions close to the optimal one remains high also for the largest instances considered in this work. Our findings keep the way open to the application of these algorithms, or variants thereof, to generate suboptimal solutions at scales relevant to industrial use-cases.

quant-ph↗
arXiv2023

Benchmarking FedAvg and FedCurv for Image Classification Tasks

Classic Machine Learning techniques require training on data available in a single data lake. However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres. Federated Learning (FL) is a distributed machine learning approach which has emerged as an effective way to address privacy concerns by only sharing local AI models while keeping the data decentralized. Two critical challenges of Federated Learning are managing the heterogeneous systems in the same federated network and dealing with real data, which are often not independently and identically distributed (non-IID) among the clients. In this paper, we focus on the second problem, i.e., the problem of statistical heterogeneity of the data in the same federated network. In this setting, local models might be strayed far from the local optimum of the complete dataset, thus possibly hindering the convergence of the federated model. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv), aiming at tackling the non-IID setting, have already been proposed. This work provides an empirical assessment of the behaviour of FedAvg and FedCurv in common non-IID scenarios. Results show that the number of epochs per round is an important hyper-parameter that, when tuned appropriately, can lead to significant performance gains while reducing the communication cost. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.

cs.LG↗
arXiv2023

Experimenting with Normalization Layers in Federated Learning on non-IID scenarios

Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacy-preserving pooling of datasets employing collaborative training from different institutions by iteratively globally aggregating locally trained models. One critical performance challenge of FL is operating on datasets not independently and identically distributed (non-IID) among the federation participants. Even though this fragility cannot be eliminated, it can be debunked by a suitable optimization of two hyper-parameters: layer normalization methods and collaboration frequency selection. In this work, we benchmark five different normalization layers for training Neural Networks (NNs), two families of non-IID data skew, and two datasets. Results show that Batch Normalization, widely employed for centralized DL, is not the best choice for FL, whereas Group and Layer Normalization consistently outperform Batch Normalization. Similarly, frequent model aggregation decreases convergence speed and mode quality.

cs.LG↗
arXiv2021

EXSCALATE: An extreme-scale in-silico virtual screening platform to evaluate 1 trillion compounds in 60 hours on 81 PFLOPS supercomputers

The social and economic impact of the COVID-19 pandemic demands the reduction of the time required to find a therapeutic cure. In the contest of urgent computing, we re-designed the Exscalate molecular docking platform to benefit from heterogeneous computation nodes and to avoid scaling issues. We deployed the Exscalate platform on two top European supercomputers (CINECA-Marconi100 and ENI-HPC5), with a combined computational power of 81 PFLOPS, to evaluate the interaction between 70 billions of small molecules and 15 binding-sites of 12 viral proteins of Sars-Cov2. The experiment lasted 60 hours and overall it performed a trillion of evaluations.

cs.DC↗
arXiv2021

Quantum ESPRESSO toward the exascale

Quantum ESPRESSO is an open-source distribution of computer codes for quantum-mechanical materials modeling, based on density-functional theory, pseudopotentials, and plane waves, and renowned for its performance on a wide range of hardware architectures, from laptops to massively parallel computers, as well as for the breadth of its applications. In this paper we present a motivation and brief review of the ongoing effort to port Quantum ESPRESSO onto heterogeneous architectures based on hardware accelerators, which will overcome the energy constraints that are currently hindering the way towards exascale computing.

physics.comp-ph↗
arXiv2019

COUNTDOWN Slack: a Run-time Library to Reduce Energy Footprint in Large-scale MPI Applications

The power consumption of supercomputers is a major challenge for system owners, users, and society. It limits the capacity of system installations, it requires large cooling infrastructures, and it is the cause of a large carbon footprint. Reducing power during application execution without changing the application source code or increasing time-to-completion is highly desirable in real-life high-performance computing scenarios. The power management run-time frameworks proposed in the last decade are based on the assumption that the duration of communication and application phases in an MPI application can be predicted and used at run-time to trade-off communication slack with power consumption. In this manuscript, we first show that this assumption is too general and leads to mispredictions, slowing down applications, thereby jeopardizing the claimed benefits. We then propose a new approach based on (i) the separation of communication phases and slack during MPI calls and (ii) a timeout algorithm to cope with the hardware power management latency, which jointly makes it possible to achieve performance-neutral power saving in MPI applications without requiring labor-intensive and risky application source code modifications. We validate our approach in a tier-1 production environment with widely adopted scientific applications. Our approach has a time-to-completion overhead lower than 1%, while it successfully exploits slack in communication phases to achieve an average energy saving of 10%. If we focus on a large-scale application runs, the proposed approach achieves 22% energy saving with an overhead of only 0.4%. With respect to state-of-the-art approaches, COUNTDOWN Slack is the only that always leads to an energy saving with negligible overhead (<3%).

cs.DC↗
arXiv2019

COUNTDOWN: a Run-time Library for Performance-Neutral Energy Saving in MPI Applications

Power and energy consumption is becoming key challenges to deploy the first exascale supercomputer successfully. Large-scale HPC applications waste a significant amount of power in communication and synchronization-related idle times. However, due to the time scale at which communication happens, transitioning in low power states during communication's idle times may introduce unacceptable overhead in applications' execution time. In this paper, we present COUNTDOWN, a runtime library, supported by a methodology and analysis tool for identifying and automatically reducing the power consumption of the computing elements during communication and synchronization. COUNTDOWN saves energy without imposing significant time-to-completion increase by lowering CPUs power consumption only during idle times for which power state transition overhead are negligible. This is done transparently to the user, without requiring labor-intensive and error-prone application code modifications, nor requiring recompilation of the application. We test our methodology in a production Tier-0 system. For the NAS benchmarks, COUNTDOWN saves between 6% and 50% energy, with a time-to-solution penalty lower than 5%. In a complete production --- Quantum ESPRESSO --- for a 3.5K cores run, COUNTDOWN saves 22.36% energy, with a performance penalty below 3%. Energy saving increases to 37% with a performance penalty of 6.38%, if the application is executed without communication tuning.

cs.DC↗
arXiv2019

The ANTAREX Domain Specific Language for High Performance Computing

The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases.

cs.DC↗
arXiv2019

Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes

In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used to perform screening of a large library of molecules, in the early phase of the process. Given the amount of candidate molecules and the complexity of the application, this task is usually performed using High-Performance Computing (HPC) platforms. In modern HPC systems, heterogeneous platforms provide a better throughput with respect to homogeneous platforms. In this work, we ported and optimized a molecular docking application to a heterogeneous system, with one or more GPU accelerators, leveraging a hybrid OpenMP and OpenACC approach. We prove that our approach has a better exploitation of the node compared to pure CPU/GPU data splitting approaches, reaching a throughput improvement up to 36% while considering the same computing node.

cs.DC↗
arXiv2019

Tunable Approximations to Control Time-to-Solution in an HPC Molecular Docking Mini-App

The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be forwarded to the later stages of the drug discovery process. Given the huge complexity of the problem, molecular docking cannot be solved by exploring the entire space of the ligand poses. State-of-the-art approaches face the problem by sampling the space of the ligand poses to generate results in a reasonable time budget. In this work, we improve the geometric approach to molecular docking by introducing tunable approximations. In particular, we analyzed and enriched the original implementation with tunable software knobs to explore and control the performance-accuracy tradeoffs. We modeled time-to-solution of the virtual screening task as a function of software knobs, input data features, and available computational resources. Therefore, the application can autotune its configuration according to a user-defined time budget. We used a Mini-App derived by LiGenDock - a state-of-the-art molecular docking application - to validate the proposed approach. We run the enhanced Mini-App on an HPC system by using a very large database of pockets and ligands. The proposed approach exposes a time-to-solution interval spanning more than one order of magnitude with accuracy degradation up to 30%, more in general providing different accuracy levels according to the needs of the virtual screening campaign.

cs.DC↗
arXiv2013

Enhancement of DFT-calculations at Petascale: Nuclear Magnetic Resonance, Hybrid Density Functional Theory and Car-Parrinello calculations

One of the most promising techniques used for studying the electronic properties of materials is based on Density Functional Theory (DFT) approach and its extensions. DFT has been widely applied in traditional solid state physics problems where periodicity and symmetry play a crucial role in reducing the computational workload. With growing compute power capability and the development of improved DFT methods, the range of potential applications is now including other scientific areas such as Chemistry and Biology. However, cross disciplinary combinations of traditional Solid-State Physics, Chemistry and Biology drastically improve the system complexity while reducing the degree of periodicity and symmetry. Large simulation cells containing of hundreds or even thousands of atoms are needed to model these kind of physical systems. The treatment of those systems still remains a computational challenge even with modern supercomputers. In this paper we describe our work to improve the scalability of Quantum ESPRESSO \cite{Quantum-Espresso} for treating very large cells and huge numbers of electrons. To this end we have introduced an extra level of parallelism, over \emph{\emph{electronic bands}}, in three kernels for solving computationally expensive problems: the Sternheimer equation solver (Nuclear Magnetic Resonance, package QE-GIPAW), the Fock operator builder (electronic ground-state, package PWscf) and most of the Car-Parrinello routines (Car-Parrinello dynamics, package CP). Final benchmarks show our success in computing the Nuclear Magnetic Response (NMR) chemical shift of a large biological assembly, the electronic structure of defected amorphous silica with hybrid exchange-correlation functionals and the equilibrium atomic structure of height Porphyrins anchored to a Carbon Nanotube, on many thousands of CPU cores.

cond-mat.mtrl-sci↗
arXiv2006

Full configuration interaction approach to the few-electron problem in artificial atoms

We present a new high-performance configuration interaction code optimally designed for the calculation of the lowest energy eigenstates of a few electrons in semiconductor quantum dots (also called artificial atoms) in the strong interaction regime. The implementation relies on a single-particle representation, but it is independent of the choice of the single-particle basis and, therefore, of the details of the device and configuration of external fields. Assuming no truncation of the Fock space of Slater determinants generated from the chosen single-particle basis, the code may tackle regimes where Coulomb interaction very effectively mixes many determinants. Typical strongly correlated systems lead to very large diagonalization problems; in our implementation, the secular equation is reduced to its minimal rank by exploiting the symmetry of the effective-mass interacting Hamiltonian, including square total spin. The resulting Hamiltonian is diagonalized via parallel implementation of the Lanczos algorithm. The code gives access to both wave functions and energies of first excited states. Excellent code scalability in a parallel environment is demonstrated; accuracy is tested for the case of up to eight electrons confined in a two-dimensional harmonic trap as the density is progressively diluted and correlation becomes dominant. Comparison with previous Quantum Monte Carlo simulations in the Wigner regime demonstrates power and flexibility of the method.

cond-mat.str-el↗