arXiv subjects

Joseph A. Insley

Publications and source records attributed to Joseph A. Insley.

5 recordsLinked to original sources

arXiv2025

Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery

Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technologies include the Intel(TM) Xeon(TM) Data Center GPU Max Series (code-named Sapphire Rapids) with support for High Bandwidth Memory (HBM), alongside the Intel(TM) Data Center GPU Max Series (code-named Ponte Vecchio) on each compute node. Aurora also integrates the Distributed Asynchronous Object Storage (DAOS), a novel exascale storage solution, and leverages Intel's oneAPI programming environment. This paper presents an in-depth exploration of Aurora's node architecture, the HPE Slingshot interconnect, the supporting software ecosystem, and DAOS. We provide insights into standard benchmark performance and applications readiness efforts via Aurora's Early Science Program and the Exascale Computing Project.

cs.DC↗

arXiv2025

Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow

Turbulent heat and momentum transfer processes due to thermal convection cover many scales and are of great importance for several natural and technical flows. One consequence is that a fully resolved three-dimensional analysis of these turbulent transfers at high Rayleigh numbers, which includes the boundary layers, is possible only using supercomputers. The visualization of these dynamics poses an additional hurdle since the thermal and viscous boundary layers in thermal convection fluctuate strongly. In order to track these fluctuations continuously, data must be tapped at high frequency for visualization, which is difficult to achieve using conventional methods. This paper makes two main contributions in this context. First, it discusses the simulations of turbulent Rayleigh-Bénard convection up to Rayleigh numbers of $Ra=10^{12}$ computed with NekRS on GPUs. The largest simulation was run on 840 nodes with 3360 GPU on the JUWELS Booster supercomputer. Secondly, an in-situ workflow using ASCENT is presented, which was successfully used to visualize the high-frequency turbulent fluctuations.

physics.flu-dyn↗

arXiv2024

Distributed Neural Representation for Reactive in situ Visualization

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

cs.DC↗

arXiv2023

Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI

In the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional checkpointing methods ineffective due to the significant slowdown in simulations while saving state data to disk. As we progress towards exascale and GPU-driven High-Performance Computing (HPC) and confront larger problem sizes, the choice becomes increasingly stark: to compromise data fidelity or to reduce resolution. To navigate this challenge, this study advocates for the use of in situ analysis and visualization techniques. These allow more frequent data "snapshots" to be taken directly from memory, thus avoiding the need for disruptive checkpointing. We detail our approach of instrumenting NekRS, a GPU-focused thermal-fluid simulation code employing the spectral element method (SEM), and describe varied in situ and in transit strategies for data rendering. Additionally, we provide concrete scientific use-cases and report on runs performed on Polaris, Argonne Leadership Computing Facility's (ALCF) 44 Petaflop supercomputer and Jülich Wizard for European Leadership Science (JUWELS) Booster, Jülich Supercomputing Centre's (JSC) 71 Petaflop High Performance Computing (HPC) system, offering practical insight into the implications of our methodology.

cs.DC↗

arXiv2011

Multiscale simulation of blood flow in brain arteries with an aneurysm

Interfacing atomistic-based with continuum-based simulation codes is now required in many multiscale physical and biological systems. We present the first results from coupled atomistic-continuum simulations on 190,000 processors. Platelet aggregation in the patient-specific model of an aneurysm has been modeled using a high-order spectral/hp element Navier-Stokes solver with a stochastic (coarse-grained) Molecular Dynamics solver based on Dissipative Particle Dynamics (DPD).

physics.flu-dyn↗