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We apply a stochastic version of the geometric (Ricci) flow, complemented with the stochastic flow of the gauge Yang-Mills sector, in order to seed the chromo-magnetic and chromo-electric vortices that source the area-law for QCD confinement. The area-law is the key signature of quark confinement in Yang-Mills gauge theories with a non-trivial center symmetry. In particular, chromo-magnetic vortices enclosed within the chromo-electric Wilson loops instantiate the area-law asymptotic behaviour of the Wilson loop vacuum expectation values. The stochastic gauge-geometry flow is responsible for the topology changes that induce the appearance of the vortices. When vortices vanish, due to topology changes in the manifolds associated to the hadronic ground states, the evaluation of the Wilson loop yields a dependence on the length of the path, hence reproducing the perimeter law of the hadronic (Higgs) phase of real QCD. Confinement, instead, is naturally achieved within this context as a by-product of the topology change of the manifold over which the dynamics of the Yang-Mills fields is defined. It is then provided by the Aharonov-Bohm effect induced by the concatenation of the compact chromo-electric and chromo-magnetic fluxes originated by the topology changes. The stochastic gauge-geometry flow naturally accomplishes a treatment of the emergence of the vortices and the generation of turbulence effects. Braiding and knotting, resulting from topology changes, namely stochastic fluctuations of the Einstein-Yang-Mills system, stabilise the chromo-magnetic vortices and dynamically induce, as non-trivial topological features, chiral symmetry-breaking. The latter is due to the appearance of torsional degrees of freedom, to which four fermion interactions are related, and to the formation of fermion condensates, in which gluon strings get stabilised by non-trivial braiding., Comment: 12 pages

Fast radio bursts (FRBs) are energetic, millisecond-duration radio pulses observed at extragalactic distances and whose origins are still a subject of heated debate. A fraction of the FRB population have shown repeating bursts, however it's still unclear whether these represent a distinct class of sources. We investigated the bursting behaviour of FRB 20220912A, one of the most active repeating FRBs known thus far. In particular, we focused on its burst energy distribution, linked to the source energetics, and its emission spectrum, with the latter directly related to the underlying emission mechanism. We monitored FRB 20220912A at $408$ MHz with the Northern Cross radio telescope and at $1.4$ GHz using the $32$-m Medicina Grueff radio telescope. Additionally, we conducted $1.2$ GHz observations taken with the upgraded Giant Meter Wave Radio Telescope (uGMRT) searching for a persistent radio source coincident with FRB 20220912A, which included high energy observations in the $0.3-10$ keV, $0.4-100$ MeV and $0.03-30$ GeV energy range. We report $16$ new bursts from FRB 20220912A at $408$ MHz during the period between October 16$^{\rm th}$ 2022 and December 31$^{\rm st}$ 2023. Their cumulative spectral energy distribution follows a power law with slope $\alpha_E = -1.3 \pm 0.2$ and we measured a repetition rate of $0.19 \pm 0.03$ hr$^{-1}$ for bursts having a fluence of $\mathcal{F} \geq 17$ Jy ms. Furthermore, we report no detections at 1.4 GHz for $\mathcal{F} \geq 20$ Jy ms. These non-detections imply an upper limit of $\beta < -2.3$, with $\beta$ being the $408$ MHz $-$ $1.4$ GHz spectral index of FRB 20220912A. This is inconsistent with positive $\beta$ values found for the only two known cases in which an FRB has been detected in separate spectral bands. We find that FRB 20220912A shows a decline of four orders of magnitude in its bursting activity at $1.4$ GHz over a .. (abridged), Comment: accepted for publication, 13 pages, 4 figures

The physical scalar product between spin-networks has been shown to be a fundamental tool in the theory of topological quantum neural networks (TQNN), which are quantum neural networks previously introduced by the authors in the context of quantum machine learning. However, the effective evaluation of the scalar product remains a bottleneck for the applicability of the theory. We introduce an algorithm for the evaluation of the physical scalar product defined by Noui and Perez between spin-network with hexagonal shape. By means of recoupling theory and the properties of the Haar integration we obtain an efficient algorithm, and provide several proofs regarding the main steps. We investigate the behavior of the TQNN evaluations on certain classes of spin-networks with the classical and quantum recoupling. All results can be independently reproduced through the "idea.deploy" framework~\href{https://github.com/lullimat/idea.deploy}{\nolinkurl{https://github.com/lullimat/idea.deploy}}, Comment: 15 pages (2 columns, 12+3), 16 figures. Comments are welcome!

In order to reconcile the wave-function collapse in quantum mechanics with the finiteness of signals' propagation in general relativity, we delve into a stochastic version of the Ricci flow and study its non-relativistic limit in presence of matter. We hence derive the Di\'osi-Penrose collapse model for the wave-function of a quantum gas. The procedure entails additional parameters with respect to phenomenological models hitherto accounted for, including the temperature of the gas and the cosmological constant, in turn related to the stochastic gravitational noise responsible for the collapse., Comment: 14 pages

Fast radio bursts (FRBs) are millisecond radio transients observed at cosmological distances. The nature of their progenitors is still a matter of debate, although magnetars are invoked by most models. The proposed FRB-magnetar connection was strengthened by the discovery of an FRB-like event from the Galactic magnetar SGR J1935+2154. In this work, we aim to investigate how prevalent magnetars such as SGR J1935+2154 are within FRB progenitors. We carried out an FRB search in a sample of seven nearby (< 12 Mpc) galaxies with the Northern Cross radio telescope for a total of 692 h. We detected one 1.8 ms burst in the direction of M101 with a fluence of $58 \pm 5$ Jy ms. Its dispersion measure of 303 pc cm$^{-3}$ places it most-likely beyond M101. Considering that no significant detection comes indisputably from the selected galaxies, we place a 38 yr$^{-1}$ upper limit on the total burst rate (i.e. including the whole sample) at the 95\% confidence level. This upper limit constrains the event rate per magnetar $\lambda_{\rm mag} < 0.42$ magnetar$^{-1}$ yr$^{-1}$ or, if combined with literature observations of a similar sample of nearby galaxies, it yields a joint constraint of $\lambda_{\rm mag} < 0.25$ magnetar$^{-1}$ yr$^{-1}$. We also provide the first constraints on the expected rate of FRBs hypothetically originating from ultraluminous X-ray (ULX) sources, since some of the galaxies observed during our observational campaign host confirmed ULXs. We obtain $< 13$ yr$^{-1}$ per ULX for the total sample of galaxies observed. Our results indicate that bursts with energies $E>10^{34}$ erg from magnetars like SGR J1935+2154 appear more rarely compared to previous observations and further disfavour them as unique progenitors for the cosmological FRB population, leaving more space open to the contribution from a population of more exotic magnetars, not born via core-collapsed supernovae., Comment: 9 pages, 4 figures, published in A&A

We quantitatively characterize the metastability in a multi-phase lattice Boltzmann model. The structure factor of density fluctuations is theoretically obtained and numerically verified to a high precision, for all simulated wave-vectors and reduced temperatures. The static structure factor is found to consistently diverge as the temperature approaches the critical-point or the density approaches the spinodal line at a sub-critical temperature. Theoretically predicted critical exponents are observed in both cases. Finally, the phase separation in the unstable branch follows the same pattern, i.e. the generation of interfaces with different topology, as observed in molecular dynamics simulations. All results can be independently reproduced through the ``idea.deploy" framework https://github.com/lullimat/idea.deploy, Comment: 5 pages, 3 figures, added Supplemental Material in ancillary files

In this paper, we present a novel data-driven optimization approach for trajectory based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users' trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, while optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible 4D trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from real air traffic data repositories. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions of the problem facilitates a consensus between network manager and airspace users. In view of the level of accuracy of the solutions and of the excellent computational performance, we are optimistic that the proposed approach may provide a significant contribution to the development of the next generation of air traffic flow management tools.

Deep Neural Networks miss a principled model of their operation. A novel framework for supervised learning based on Topological Quantum Field Theory that looks particularly well suited for implementation on quantum processors has been recently explored. We propose using this framework to understand the problem of generalisation in Deep Neural Networks. More specifically, in this approach, Deep Neural Networks are viewed as the semi-classical limit of Topological Quantum Neural Networks. A framework of this kind explains the overfitting behavior of Deep Neural Networks during the training step and the corresponding generalisation capabilities. We explore the paradigmatic case of the perceptron, which we implement as the semiclassical limit of Topological Quantum Neural Networks. We apply a novel algorithm we developed, showing that it obtains similar results to standard neural networks, but without the need for training (optimisation)., Comment: 22 pages (two columns), 9 figures. v2: Several parts rewritten, and computational results added

In this work we report the results of a nineteen-month Fast Radio Burst observational campaign carried out with the North-South arm of the Medicina Northern Cross radio telescope at 408~MHz in which we monitored four repeating sources: FRB20180916B, FRB20181030A, FRB20200120E and FRB20201124A. We present the current state of the instrument and the detection and characterisation of three bursts from FRB20180916B. Given our observing time, our detections are consistent with the event number we expect from the known burst rate ($2.7 \pm 1.9$ above our 10$\sigma$, 38~Jy~ms detection threshold) in the 5.2 day active window of the source, further confirming the source periodicity. We detect no bursts from the other sources. We turn this result into a 95\% confidence level lower limit on the slope of the differential fluence distribution $\alpha$ to be $\alpha > 2.1$ and $\alpha > 2.2$ for FRB20181030A and FRB20200120E respectively. Given the known rate for FRB20201124A, we expect $1.0 \pm 1.1$ bursts from our campaign, consistent with our non-detection., Comment: MNRAS Accepted, 10 pages, 6 figures

In this paper we analyze the curvature corrections to the surface tension in the context of the Shan-Chen (SC) multi-component Lattice Boltzmann method (LBM). We demonstrate that the same techniques recently applied in the context of the Shan-Chen multi-phase model can be applied to multi-component mixtures. We implement, as a new application, the calculation of the surface of tension radius $R_s$ through the minimization of the generalized surface tension $\sigma[R]$. In turn we are able to estimate the Tolman length, i.e. the first order coefficient of the curvature expansion of the surface tension $\sigma(R)$, as well as the higher order corrections, i.e. the curvature- and the Gaussian-rigidity coefficients. The SC multi-component model allows to model both fully-symmetric as well as asymmetric interactions among the components. By performing an extensive set of simulations we present a first example of tunable Tolman length in the mesoscopic model, being zero for symmetric interactions and different from zero otherwise. This result paves the way for controlling such interface properties which are paramount in presence of thermal fluctuations. All reported results can be independently reproduced through the "idea.deploy" framework available at https://github.com/lullimat/idea.deploy., Comment: updated after Referee review; 22 pages, 4 figures. arXiv admin note: text overlap with arXiv:2105.08772

We follow a new pathway to the definition of the Stochastic Quantization (SQ), first proposed by Parisi and Wu, of the action functional yielding the Einstein equations. Hinging on the functional similarities between the Ricci-Flow equation and the SQ Langevin equations proposed by Rumpf, we push forward a novel approach with multiplicative noise and a stochastic time that converges to the proper time of a space-like foliation in the equilibrium limit. Furthermore, we express the starting system of equations using the Arnowitt-Deser-Misner (ADM) variables and their conjugated Hamiltonian momenta. Such a choice is instrumental in understanding the newly derived equations in terms of the breakdown of the diffeomorphism invariance of the classical theory, which instead will hold on average at the steady state. We comment on the physical interpretation of the Ricci flow equations, and argue how they can naturally provide, in a geometrical way, the renormalization group equation for gravity theories. In the general setting, the equation associated to the shift vector yields the Navier-Stokes equation with a stochastic source. Moreover, we show that the fluctuations of the metric tensor components around the equilibrium configurations, far away from the horizon of a Schwarzschild black hole, are forced by the Ricci flow to follow the Kardar-Parisi-Zhang equation, whose probabilistic distribution can yield an intermittent statistics. We finally comment on the possible applications of this novel scenario to the cosmological constant, arguing that the Ricci flow may provide a solution to the Hubble tension, as a macroscopic effect of scale dependence of the quantum fluctuations of the metric tensor., Comment: 15 pages; all sections have been revised

The goal of Optimal Transmission Switching (OTS) problem for power systems is to identify a topology of the power grid that minimizes the cost of the system operation while satisfying the operational and physical constraints. Among the most popular methods to solve OTS is to construct approximation via integer linear programming formulations, which often come with big-M inequalities. These big-M inequalities increase, considerably, the difficulty of solving the resulting formulations. Moreover, choosing big-M values optimally is as hard as solving OTS itself. In this paper, we devise two data-driven big-M bound strengthening methods which take network structure, power demands and generation costs into account. We illustrate the robustness of our methods to load changes and impressive runtime improvements of mixed-integer solvers achieved by our methods with extensive experiments on benchmark instances. The speedup by one of the proposed methods is almost 13 times with respect to the exact method.

In this paper, we present TLBfind, a GPU code for simulating the hydrodynamics of droplets along with a dynamic temperature field. TLBfind hinges on a two-dimensional multi-component lattice Boltzmann (LB) model simulating a concentrated emulsion with finite-size droplets evolving in a thermal convective state, just above the transition from conduction to convection. The droplet concentration of the emulsion system is tenable, and at the core of the code lies the possibility to measure a large number of physical observables characterising the flow and droplets. Furthermore, TLBfind includes a parallel implementation on GPU of the Delaunay triangulation useful for the detection of droplets' plastic rearrangements, and several types of boundary conditions, supporting simulations of channels with structured rough walls.

We demonstrate that the multi-phase Shan-Chen lattice Boltzmann method (LBM) yields a curvature dependent surface tension $\sigma$ as computed from three-dimensional hydrostatic droplets/bubbles simulations. Such curvature dependence is routinely characterized, at first order, by the so-called {\it Tolman length} $\delta$. LBM allows to precisely compute $\sigma$ at the surface of tension $R_s$ and determine the Tolman length from the coefficient of the first order correction. The corresponding values of $\delta$ display universality for different equations of state, following a power-law scaling near the critical temperature. The Tolman length has been studied so far mainly via computationally demanding molecular dynamics (MD) simulations or by means of density functional theory (DFT) approaches playing a pivotal role in extending Classical Nucleation Theory. The present results open a new hydrodynamic-compliant mesoscale arena, in which the fundamental role of the Tolman length, alongside real-world applications to cavitation phenomena, can be effectively tackled. All the results can be independently reproduced through the "idea.deploy" framework., Comment: 10 pages, 5 figures: extended text and added figures

We systematically analyze the tensorial structure of the lattice pressure tensors for a class of multi-phase lattice Boltzmann models (LBM) with multi-range interactions. Due to lattice discrete effects, we show that the built-in isotropy properties of the lattice interaction forces are not necessarily mirrored in the corresponding lattice pressure tensor. This finding opens a different perspective for constructing forcing schemes, achieving the desired isotropy in the lattice pressure tensors via a suitable choice of multi-range potentials. As an immediate application, the obtained LBM forcing schemes are tested via numerical simulations of non-ideal equilibrium interfaces and are shown to yield weaker and less spatially extended spurious currents with respect to forcing schemes obtained by forcing isotropy requirements only. From a general perspective, the proposed analysis yields an approach for implementing forcing symmetries, never explored so far in the framework of the Shan-Chen method for LBM. We argue this will be beneficial for future studies of non-ideal interfaces., Comment: 14 pages + Appendix, 8 figures; updated to published version: added figures and text

Our work intends to show that: (1) Quantum Neural Networks (QNN) can be mapped onto spinnetworks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theories (TQFT); (2) Deep Neural Networks (DNN) are a subcase of QNN, in the sense that they emerge as the semiclassical limit of QNN; (3) A number of Machine Learning (ML) key-concepts can be rephrased by using the terminology of TQFT. Our framework provides as well a working hypothesis for understanding the generalization behavior of DNN, relating it to the topological features of the graphs structures involved., Comment: 25 pages, 10 figures

We show that a generalized version of the holographic principle can be derived from the Hamiltonian description of information flow within a quantum system that maintains a separable state. We then show that this generalized holographic principle entails a general principle of gauge invariance. When this is realized in an ambient Lorentzian space-time, gauge invariance under the Poincare group is immediately achieved. We apply this pathway to retrieve the action of gravity. The latter is cast a la Wilczek through a similar formulation derived by MacDowell and Mansouri, which involves the representation theory of the Lie groups SO(3,2) and SO(4,1)., Comment: 26 pages, 1 figure

The Kovacs effect is a remarkable feature of the ageing dynamics of glass forming liquids near the glass transition temperature. It consists in a non-monotonous evolution of the volume/enthalpy after a succession of two abrupt temperature changes: first from a high initial temperature $T_i$ to a much lower annealing temperature $T_a$ followed by a smaller second jump back to a slightly higher final temperature $T_f$. The second change is performed when the instantaneous value of the volume/enthalpy coincides with the equilibrium one at the final temperature. While this protocol might be expected to yield equilibrium dynamics right after the second temperature change, one observes the so-called Kovacs hump in glassy systems. In this paper we apply such thermal protocol to the Distinguishable Particles Lattice Model (DPLM) for a wide range of fragility of the system. We study the Kovacs hump based on energy relaxation and all main experimental features are captured. Results are compared to general predictions based on a master equation approach in the linear response limit. We trace the origin of the Kovacs hump to the non-equilibrium nature of the probability distribution of particle interaction energies after the annealing and find that its difference with respect to the final equilibrium distribution is non-vanishing with two isolated zeros. This allows Kovacs' condition of equilibrium total energy to be met out-of-equilibrium, thus representing the memory content of the system. Furthermore, the hump is taller and occurs at a larger overlap with the system initial configuration for more fragile systems. The dynamics of a structural temperature for the mobile regions strongly depends on the glass fragility while for the immobile ones only a weak dependence is found., Comment: 9 pages, 9 figures: Added fragility study and encoding of the memory in the particles interaction energy distribution; added doi

Volume and enthalpy relaxation of glasses after a sudden temperature change has been extensively studied since Kovacs seminal work. One observes an asymmetric approach to equilibrium upon cooling versus heating and, more counter-intuitively, the expansion gap paradox, i.e. a dependence on the initial temperature of the effective relaxation time even close to equilibrium when heating. Here we show that a distinguishable-particles lattice model can capture both the asymmetry and the expansion gap. We quantitatively characterize the energetic states of the particles configurations using a physical realization of the fictive temperature called the structural temperature, which, in the heating case, displays a strong spatial heterogeneity. The system relaxes by nucleation and expansion of warmer mobile domains having attained the final temperature, against cooler immobile domains maintained at the initial temperature. A small population of these cooler regions persists close to equilibrium, thus explaining the paradox., Comment: Main text: 6 pages, 3 figures. Supplemental: 2 pages, 2 figures and 2 videos

We perform kinetic Monte Carlo simulations of a distinguishable-particle lattice model of structural glasses with random particle interactions. By varying the interaction distribution and the average particle hopping energy barrier, we obtain an extraordinarily wide range of kinetic fragility. A stretching exponent, characterizing structural relaxation, is found to decrease with the kinetic fragility in agreement with experiments. The most fragile glasses are those exhibiting low hopping barriers and, more importantly, dramatic drops of entropies upon cooling toward the glass transition temperatures. The entropy drops reduce possible kinetic pathways and lead to dramatic slowdowns in the dynamics. In addition, the kinetic fragility is shown to correlate with a thermodynamic fragility., Comment: Main text: 6 pages with 4 figures Supplementary: 11 pages with 18 figures

We explore the impact of geometrical corrugations on the near-wall flow properties of a soft-material driven in a confined rough microchannel. By means of numerical simulations, we perform a quantitative analysis of the relation between the flow rate $\Phi$ and the wall stress $\sigma_w$ for a number of setups, by changing both the roughness values as well as the roughness shape. Roughness suppresses the flow, with the existence of a characteristic value of $\sigma_w$ at which flow sets in. Just above the onset of flow, we quantitatively analyze the relation between $\Phi$ and $\sigma_w$. While for smooth walls a linear dependency is observed, steeper behaviours are found to set in by increasing wall roughness. The variation of the steepness, in turn, depends on the shape of the wall roughness, wherein gentle steepness changes are promoted by a variable space localization of the roughness.

We report an analytical evaluation of the mean-squared displacement (MSD) of the particles in glasses based on their coarse grained trajectories. The calculation is conducted by means of a local random configuration-tree theory that was recently proposed by one of us [C.-H. Lam, J. Stat. Mech. \textbf{2018}, 023301 (2018)]. Results are compared with the numerical simulations of a lattice glass model, and good quantitative agreement has been obtained over a wide range of temperatures in the entire region of time with virtually no free parameters. To the best of our knowledge, the calculation is the first in its kind., Comment: 11 pages, 2 figures

We study the solid-to-liquid transition in a two-dimensional fully periodic soft-glassy model with an imposed spatially heterogeneous stress. The model we consider consists of droplets of a dispersed phase jammed together in a continuous phase. When the peak value of the stress gets close to the yield stress of the material, we find that the whole system intermittently tunnels to a metastable "fluidized" state, which relaxes back to a metastable "solid" state by means of an elastic-wave dissipation. This macroscopic scenario is studied through the microscopic displacement field of the droplets, whose time statistics displays a remarkable bimodality. Metastability is rooted in the existence, in a given stress range, of two distinct stable rheological branches as well as long-range correlations (e.g., large dynamic heterogeneity) developed in the system. Finally, we show that a similar behavior holds for a pressure-driven flow, thus suggesting possible experimental tests., Comment: 13 pages, 11 figures

This paper presents an exhaustive study on the arrivals process at eight important European airports. Using inbound traffic data, we define, compare, and contrast a data-driven Poisson and PSRA point process. Although, there is sufficient evidence that the interarrivals might follow an exponential distribution, this finding does not directly translate to evidence that the arrivals stream is Poisson. The main reason is that finite-capacity constraints impose a correlation structure to the arrivals stream, which a Poisson model cannot capture. We show the weaknesses and somehow the difficulties of using a Poisson process to model with good approximation the arrivals stream. On the other hand, our innovative non-parametric, data-driven PSRA model, predicts quite well and captures important properties of the typical arrivals stream.

The motion of soft-glassy materials (SGM) in a confined geometry is strongly impacted by surface roughness. However, the effect of the spatial distribution of the roughness remains poorly understood from a more quantitative viewpoint. Here we present a comprehensive study of concentrated emulsions flowing in microfluidic channels, one wall of which is patterned with micron-size equally spaced grooves oriented perpendicularly to the flow direction. We show that roughness-induced fluidization can be quantitatively tailored by systematically changing both the width and separation of the grooves. We find that a simple scaling law describes such fluidization as a function of the density of grooves, suggesting common scenarios for droplet trapping and release. Numerical simulations confirm these views and are used to elucidate the relation between fluidization and the rate of plastic rearrangements., Comment: 4 Figures. Supplementary Material. 2 Movies

The Voronoi diagrams are an important tool having theoretical and practical applications in a large number of fields. We present a new procedure, implemented as a set of CUDA kernels, which detects, in a general and efficient way, topological changes in case of dynamic Voronoi diagrams whose generating points move in time. The solution that we provide has been originally developed to identify plastic events during simulations of soft-glassy materials based on a Lattice Boltzmann model with frustrated-short range attractive and mid/long-range repulsive-interactions. Along with the description of our approach, we present also some preliminary physics results., Comment: 25 pages, 11 figures

By means of mesoscopic numerical simulations of a model soft-glassy material, we investigate the role of boundary roughness on the flow behaviour of the material, probing the bulk/wall and global/local rheologies. We show that the roughness reduces the wall slip induced by wettability properties and acts as a source of fluidisation for the material. A direct inspection of the plastic events suggests that their rate of occurrence grows with the fluidity field, reconciling our simulations with kinetic elasto-plastic descriptions of jammed materials. Notwithstanding, we observe qualitative and quantitative differences in the scaling, depending on the distance from the rough wall and on the imposed shear. The impact of roughness on the orientational statistics is also studied.

We present a new dynamic off-equilibrium method for the study of continuous transitions, which represents a dynamic generalization of the usual equilibrium cumulant method. Its main advantage is that critical parameters are derived from numerical data obtained much before equilibrium has been attained. Therefore, the method is particularly useful for systems with long equilibration times, like spin glasses. We apply it to the three-dimensional Ising spin-glass model, obtaining accurate estimates of the critical exponents and of the critical temperature with a limited computational effort., Comment: 5 pages, 2 figures; Two references have been added and a few typos have been corrected

We present a highly optimized implementation of a Monte Carlo (MC) simulator for the three-dimensional Ising spin-glass model with bimodal disorder, i.e., the 3D Edwards-Anderson model running on CUDA enabled GPUs. Multi-GPU systems exchange data by means of the Message Passing Interface (MPI). The chosen MC dynamics is the classic Metropolis one, which is purely dissipative, since the aim was the study of the critical off-equilibrium relaxation of the system. We focused on the following issues: i) the implementation of efficient access patterns for nearest neighbours in a cubic stencil and for lagged-Fibonacci-like pseudo-Random Numbers Generators (PRNGs); ii) a novel implementation of the asynchronous multispin-coding Metropolis MC step allowing to store one spin per bit and iii) a multi-GPU version based on a combination of MPI and CUDA streams. We highlight how cubic stencils and PRNGs are two subjects of very general interest because of their widespread use in many simulation codes. Our code best performances ~3 and ~5 psFlip on a GTX Titan with our implementations of the MINSTD and MT19937 respectively., Comment: 39 pages, 13 figures