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Memory and rejuvenation effects in the magnetic response of off-equilibrium spin glasses have been widely regarded as the doorway into the experimental exploration of ultrametricity and temperature chaos. Unfortunately, despite more than twenty years of theoretical efforts following the experimental discovery of memory and rejuvenation, these effects have, thus far, been impossible to reliably simulate. Yet, three recent developments convinced us to accept this challenge: first, the custom-built Janus II supercomputer makes it possible to carry out simulations in which the very same quantities that can be measured in single crystals of CuMn are computed from the simulation, allowing for a parallel analysis of the simulation and experimental data. Second, Janus II simulations have taught us how numerical and experimental length scales should be compared. Third, we have recently understood how temperature chaos materializes in aging dynamics. All these three aspects have proved crucial for reliably reproducing rejuvenation and memory effects on the computer. Our analysis shows that at least three different length scales play a key role in aging dynamics, whereas essentially all the theoretical analyses of the aging dynamics emphasize the presence and crucial role of a single glassy correlation length.Reliably probing rejuvenation and memory effects in spin glasses by means of simulations is difficult. Now, a state-of-the-art numerical study shows that at least three different length scales play a crucial role in aging dynamics of spin glasses.

In this paper we address the problem of identifying and exploiting techniques that optimize the performance of large scale scientific codes on many-core processors. We consider as a test-bed a state-of-the-art Lattice Boltzmann (LB) model, that accurately reproduces the thermo-hydrodynamics of a 2D-fluid obeying the equations of state of a perfect gas. The regular structure of Lattice Boltzmann algorithms makes it relatively easy to identify a large degree of available parallelism; the challenge is that of mapping this parallelism onto processors whose architecture is becoming more and more complex, both in terms of an increasing number of independent cores and – within each core – of vector instructions on longer and longer data words. We take as an example the Intel Sandy Bridge micro-architecture, that supports AVX instructions operating on 256-bit vectors; we address the problem of efficiently implementing the key computational kernels of LB codes – streaming and collision – on this family of processors; we introduce several successive optimization steps and quantitatively assess the impact of each of them on performance. Our final result is a production-ready code already in use for large scale simulations of the Rayleigh-Taylor instability. We analyze both raw performance and scaling figures, and compare with GPU-based implementations of similar codes.

Lowest level (sometimes called Level 0, L0) triggers are fundamental components in high energy physics experiments, and yet they are quite often custom-made. Even when using FPGAs to achieve better flexibility in modifying and maintaining, small changes require hardware reconfiguration and changes to the algorithm logic could be constrained by the hardware. For these reasons we are developing for the NA62 experiment at CERN a L0-trigger based on the use of a PC and commodity FPGA development board.

A recent trend in scientific computing is the increasingly important role of co-processors, originally built to accelerate graphics rendering, and now used for general high-performance computing. The INFN Computing On Knights and Kepler Architectures (COKA) project focuses on assessing the suitability of co-processor boards for scientific computing in a wide range of physics applications, and on studying the best programming methodologies for these systems. Here we present in a comparative way our results in porting a Lattice Boltzmann code on two state-of-the-art accelerators: the NVIDIA K20X, and the Intel Xeon-Phi. We describe our implementations, analyze results and compare with a baseline architecture adopting Intel Sandy Bridge CPUs.

A special feature of Rayleigh-Taylor systems with chemical reactions is the competition between turbulent mixing and the \"burning processes\", which leads to a highly non-trivial dynamics. We studied the problem performing high resolution numerical simulations of a 2d system, using a thermal lattice Boltzmann (LB) model. We spanned the various regimes emerging at changing the relative chemical/turbulent time scales, from slow to fast reaction; in the former case we found numerical evidence of an enhancement of the front propagation speed (with respect to the laminar case), providing a phenomenological argument to explain the observed behaviour. When the reaction is very fast, instead, the formation of sharp fronts separating patches of pure phases, leads to an increase of intermittency in the small scale statistics of the temperature field.

The parameterization of small-scale turbulent fluctuations in convective systems and in the presence of strong stratification is important for many applied problems in oceanography, atmospheric science and planetology. In the presence of stratification, both bulk turbulent fluctuations and inversion regions, where temperature, density –or both– develop highly nonlinear mean profiles, are crucial. We present a second order closure able to reproduce simultaneously both bulk and boundary layer regions. We test it using high-resolution state-of-the-art 2D numerical simulations in a Rayleigh-Taylor convective and stratified belt for values of the Rayleigh number, up to Ra ∼ 109. The system is confined by the existence of an adiabatic gradient. Our numerical simulations are performed using a thermal Lattice Boltzmann Method (Sbragaglia et al, 2009) able to reproduce the Navier-Stokes equations for momentum, density and internal energy (see also (Biferale et al, 2011b) for an extension to a case with forcing on internal energy). Validation of the method can be found in (Biferale et al, 2010; Scagliarini et al, 2010). Here we present numerical simulations up to 4096 × 10000 grid points obtained on the QPACE supercomputer (Goldrian et al, 2008).

We unveil the multifractal behavior of Ising spin glasses in their low-temperature phase. Using the Janus II custom-built supercomputer, the spin-glass correlation function is studied locally. Dramatic fluctuations are found when pairs of sites at the same distance are compared. The scaling of these fluctuations, as the spin-glass coherence length grows with time, is characterized through the computation of the singularity spectrum and its corresponding Legendre transform. A comparatively small number of site pairs controls the average correlation that governs the response to a magnetic field. We explain how this scenario of dramatic fluctuations (at length scales smaller than the coherence length) can be reconciled with the smooth, self-averaging behavior that has long been considered to describe spin-glass dynamics.

The extended principle of superposition has been a touchstone of spin glass dynamics for almost thirty years. The Uppsala group has demonstrated its validity for the metallic spin glass, CuMn, for magnetic fields \\(H\\) up to 10 Oe at the reduced temperature \\(T_r=T/T_g = 0.95\\), where \\(T_g\\) is the spin glass condensation temperature. For \\(H > 10\\) Oe, they observe a departure from linear response which they ascribe to the development of non-linear dynamics. The thrust of this paper is to develop a microscopic origin for this behavior by focusing on the time development of the spin glass correlation length, \\((t,t_w;H)\\). Here, \\(t\\) is the time after \\(H\\) changes, and \\(t_w\\) is the time from the quench for \\(T>T_g\\) to the working temperature \\(T\\) until \\(H\\) changes. We connect the growth of \\((t,t_w;H)\\) to the barrier heights \\((t_w)\\) that set the dynamics. The effect of \\(H\\) on the magnitude of \\((t_w)\\) is responsible for affecting differently the two dynamical protocols associated with turning \\(H\\) off (TRM, or thermoremanent magnetization) or on (ZFC, or zero field-cooled magnetization). In this paper, we display the difference between the zero-field cooled \\(_ ZFC(t,t_w;H)\\) and the thermoremanent magnetization \\(_ TRM(t,t_w;H)\\) correlation lengths as \\(H\\) increases, both experimentally and through numerical simulations, corresponding to the violation of the extended principle of superposition in line with the finding of the Uppsala Group.

Memory and rejuvenation effects in the magnetic response of off-equilibrium spin glasses have been widely regarded as the doorway into the experimental exploration of ultrametricity and temperature chaos (maybe the most exotic features in glassy free-energy landscapes). Unfortunately, despite more than twenty years of theoretical efforts following the experimental discovery of memory and rejuvenation, these effects have thus far been impossible to simulate reliably. Yet, three recent developments convinced us to accept this challenge: first, the custom-built Janus II supercomputer makes it possible to carry out \"numerical experiments\" in which the very same quantities that can be measured in single crystals of CuMn are computed from the simulation, allowing for parallel analysis of the simulation/experiment data. Second, Janus II simulations have taught us how numerical and experimental length scales should be compared. Third, we have recently understood how temperature chaos materializes in aging dynamics. All three aspects have proved crucial for reliably reproducing rejuvenation and memory effects on the computer. Our analysis shows that (at least) three different length scales play a key role in aging dynamics, while essentially all theoretical analyses of the aging dynamics emphasize the presence and the crucial role of a single glassy correlation length.

Energy consumption is increasingly becoming a limiting factor to the design of faster large-scale parallel systems, and development of energy-efficient and energy-aware applications is today a relevant issue for HPC code-developer communities. In this work we focus on energy performance of the Knights Landing (KNL) Xeon Phi, the latest many-core architecture processor introduced by Intel into the HPC market. We take into account the 64-core Xeon Phi 7230, and analyze its energy performance using both the on-chip MCDRAM and the regular DDR4 system memory as main storage for the application data-domain. As a benchmark application we use a Lattice Boltzmann code heavily optimized for this architecture and implemented using different memory data layouts to store its lattice. We assessthen the energy consumption using different memory data-layouts, kind of memory (DDR4 or MCDRAM) and number of threads per core.