arXiv subjects

Salvatore Cielo

Publications and source records attributed to Salvatore Cielo.

9 recordsLinked to original sources

arXiv2026

Node-Level Performance and Energy Characterization of Flagship Science Applications on SuperMUC-NG Phase 2

We present a systematic performance and energy-efficiency characterization of five flagship scientific workloads on SuperMUC-NG phase 2, the 28 PetaFLOPs system at the Leibniz Supercomputing Center (LRZ) equipped with Intel Xeon Platinum 8480+ and Intel Data Center GPU Max 1550 (Ponte Vecchio, PVC) accelerators. The selected codes span molecular dynamics (gromacs, lammps), astrophysics and cosmology (OpenGadget3, AthenaK), and finite-element PDE solvers from the dealii-X Center of Excellence. For each code we measure throughput and energy efficiency expressed as compute-elements per wall-clock second (or per Joule of consumed energy) on a single compute node, comparing CPU-only (SPR) against combined CPU+GPU (SPR+PVC) configurations where available. Energy measurements rely on lightweight code instrumentation with p3em, or the Energy Aware Runtime (EAR) present on the system. Our results show that GPU offload yields $4-12\times$ higher throughput and up to $15\times$ better energy efficiency compared to CPU-only execution, with lammps and AthenaK benefiting most. However, both throughput and energy gains are sensitive to problem granularity: insufficient work per GPU tile erodes the accelerator advantage, as clearly observed in AthenaK at small mesh-block sizes. The power-budget utilization is systematically lower for CPUs than it is for GPUs, indicating that even at peak useful-work rate, most applications running on CPUs leave a significant fraction of the node's thermal envelope unused.

cs.DC↗

arXiv2026

Towards FAIR Astrophysical Simulations

Reproducibility is a cornerstone of science. FAIR (findable, accessible, interoperable, and reusable) data is often a vital step towards testing the reproducibility of results. The implementation of FAIR principles in the astrophysical simulation community is still varied. We approach the discussion of this topic mainly from a high-performance computing (HPC) point of view. We identify the main obstacles to FAIR astrophysics simulations: First, the vast datasets created in simulations on HPC facilities complicate FAIR data management. Second, missing incentives to fully share codes, results, and diagnostic data. Third, a lack of workflows that include data publication and technical support. Therefore, particularly smaller research groups struggle due to the unavailability of dedicated personnel and time in their efforts towards FAIR and open simulations. We propose actionable steps towards achieving ``FAIRer'' data and open source publication standards in numerical astrophysics. Our suggestions include low-threshold methods to fulfil the basic FAIR requirements as well as basic tools for FAIR (meta-)data generation and data/code publication. This work is a high-level overview intended to initiate discussions within the community, offering initial solutions to these challenges.

astro-ph.IM↗

arXiv2025

SYCL for Energy-Efficient Numerical Astrophysics: the case of DPEcho

Energy awareness and efficiency policies are gaining more attention, over pure performance (time-to-solution) Key Performance Indicators (KPIs) when comparing the possibilities offered by accelerated systems. But in a field such as numerical astrophysics, which is struggling with code refactorings for GPUs, viable porting paths have to be shown before first. After summarizing the status and recurring problems of astrophysical code accelerations, we highlight how the field would benefit from portable, vendor-agnostic GPU portings. We then employ the DPEcho SYCL benchmark to compare raw performance and energy efficiency for heterogeneous hardware on a realistic application, with the goal of helping computational astrophysicists and HPC providers make informed decisions on the most suitable hardware. Aside from GPUs showing higher efficiency, we argue on the more informative nature of energy-aware KPIs, in that they convey the specific device performance in a data-driven way. We also present a convenient, flexible and cross-platform energy-measuring pipeline. Finally, we contextualize our results through measures with different compilers, presenting device (at the cores) versus node (at the plug) energy and comparing DPEcho with the High- Performance Linpack (HPL) benchmark.

astro-ph.IM↗

arXiv2025

The spectrum of magnetized turbulence in the interstellar medium

The interstellar medium (ISM) of our Galaxy is magnetized, compressible and turbulent, influencing many key ISM properties, like star formation, cosmic ray transport, and metal and phase mixing. Yet, basic statistics describing compressible, magnetized turbulence remain uncertain. Utilizing grid resolutions up to $10,080^3$ cells, we simulate highly-compressible, magnetized ISM-style turbulence with a magnetic field maintained by a small-scale dynamo. We measure two coexisting kinetic energy cascades, $\mathcal{E}_{\rm kin}(k) \propto k^{-n}$, in the turbulence, separating the plasma into scales that are non-locally interacting, supersonic and weakly magnetized $(n=2.01\pm 0.03\approx 2)$ and locally interacting, subsonic and highly magnetized $(n=1.465\pm 0.002\approx 3/2)$, where $k$ is the wavenumber. We show that the $3/2$ spectrum can be explained with scale-dependent kinetic energy fluxes and velocity-magnetic field alignment. On the highly magnetized modes, the magnetic energy spectrum forms a local cascade $(n=1.798\pm 0.001\approx 9/5)$, deviating from any known \textit{ab initio} theory. With a new generation of radio telescopes coming online, these results provide a means to directly test if the ISM in our Galaxy is maintained by the compressible turbulent motions from within it.

astro-ph.GA↗

arXiv2024

Magnetized compressible turbulence with a fluctuation dynamo and Reynolds numbers over a million

Supersonic magnetohydrodynamic (MHD) turbulence is a ubiquitous state for many astrophysical plasmas. However, even the basic statistics for this type of turbulence remains uncertain. We present results from supersonic MHD turbulence simulations at unparalleled resolutions, with plasma Reynolds numbers of over a million. In the kinetic energy spectrum we find a break between the scales that are dominated by kinetic energy, with spectral index $-2$, and those that become strongly magnetized, with spectral index $-3/2$. By analyzing the Helmholtz decomposed kinetic energy spectrum, we find that the compressible modes are not passively mixed through the cascade of the incompressible modes. At high magnetic Reynolds number, above $10^5$, we find a power law in the magnetic energy spectrum with spectral index $-9/5$. On the strongly magnetized, subsonic scales the plasma tends to self-organize into locally relaxed regions, where there is strong alignment between the current density, magnetic field, velocity field and vorticity field, depleting both the nonlinearities and magnetic terms in the MHD equations, which we attribute to plasma relaxation on scales where the magnetic fluctuations evolve on shorter timescales than the velocity fluctuations. This process constrains the cascade to inhomogenous, volume-poor, fractal surfaces between relaxed regions, which has significant repercussions for understanding the nature of magnetized turbulence in astrophysical plasmas and the saturation of the fluctuation dynamo.

astro-ph.GA↗

arXiv2021

Optimizing the hybrid parallelization of BHAC

We present our experience with the modernization on the GR-MHD code BHAC, aimed at improving its novel hybrid (MPI+OpenMP) parallelization scheme. In doing so, we showcase the use of performance profiling tools usable on x86 (Intel-based) architectures. Our performance characterization and threading analysis provided guidance in improving the concurrency and thus the efficiency of the OpenMP parallel regions. We assess scaling and communication patterns in order to identify and alleviate MPI bottlenecks, with both runtime switches and precise code interventions. The performance of optimized version of BHAC improved by $\sim28\%$, making it viable for scaling on several hundreds of supercomputer nodes. We finally test whether porting such optimizations to different hardware is likewise beneficial on the new architecture by running on ARM A64FX vector nodes.

cs.DC↗

arXiv2020

Honing and proofing Astrophysical codes on the road to Exascale. Experiences from code modernization on many-core systems

The complexity of modern and upcoming computing architectures poses severe challenges for code developers and application specialists, and forces them to expose the highest possible degree of parallelism, in order to make the best use of the available hardware. The Intel$^{(R)}$ Xeon Phi$^{(TM)}$ of second generation (code-named Knights Landing, henceforth KNL) is the latest many-core system, which implements several interesting hardware features like for example a large number of cores per node (up to 72), the 512 bits-wide vector registers and the high-bandwidth memory. The unique features of KNL make this platform a powerful testbed for modern HPC applications. The performance of codes on KNL is therefore a useful proxy of their readiness for future architectures. In this work we describe the lessons learnt during the optimisation of the widely used codes for computational astrophysics P-Gadget-3, Flash and Echo. Moreover, we present results for the visualisation and analysis tools VisIt and yt. These examples show that modern architectures benefit from code optimisation at different levels, even more than traditional multi-core systems. However, the level of modernisation of typical community codes still needs improvements, for them to fully utilise resources of novel architectures.

cs.DC↗

arXiv2019

Visualizing the world's largest turbulence simulation

In this exploratory submission we present the visualization of the largest interstellar turbulence simulations ever performed, unravelling key astrophysical processes concerning the formation of stars and the relative role of magnetic fields. The simulations, including pure hydrodynamical (HD) and magneto-hydrodynamical (MHD) runs, up to a size of $10048^3$ grid elements, were produced on the supercomputers of the Leibniz Supercomputing Centre and visualized using the hybrid parallel (MPI+TBB) ray-tracing engine OSPRay associated with VisIt. Besides revealing features of turbulence with an unprecedented resolution, the visualizations brilliantly showcase the stretching-and-folding mechanisms through which astrophysical processes such as supernova explosions drive turbulence and amplify the magnetic field in the interstellar gas, and how the first structures, the seeds of newborn stars are shaped by this process.

physics.comp-ph↗

arXiv2019

Speeding simulation analysis up with yt and Intel Distribution for Python

As modern scientific simulations grow ever more in size and complexity, even their analysis and post-processing becomes increasingly demanding, calling for the use of HPC resources and methods. yt is a parallel, open source post-processing python package for numerical simulations in astrophysics, made popular by its cross-format compatibility, its active community of developers and its integration with several other professional Python instruments. The Intel Distribution for Python enhances yt's performance and parallel scalability, through the optimization of lower-level libraries Numpy and Scipy, which make use of the optimized Intel Math Kernel Library (Intel-MKL) and the Intel MPI library for distributed computing. The library package yt is used for several analysis tasks, including integration of derived quantities, volumetric rendering, 2D phase plots, cosmological halo analysis and production of synthetic X-ray observation. In this paper, we provide a brief tutorial for the installation of yt and the Intel Distribution for Python, and the execution of each analysis task. Compared to the Anaconda python distribution, using the provided solution one can achieve net speedups up to 4.6x on Intel Xeon Scalable processors (codename Skylake).

astro-ph.IM↗