Pith. sign in

REVIEW 3 major objections 5 minor 38 references

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

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims the first direct numerical simulation of Rayleigh-Bénard convection at Ra=10^12 with Γ=4, combined with fully time-resolved in-situ visualization of the boundary-layer dynamics.

desk verdict Solid HPC engineering paper whose workflow claim holds up; the Ra=1e12 physics claims need tightening before the numbers are quoted. read the letter →

arxiv 2501.13240 v1 pith:JT47RYJV submitted 2025-01-22 physics.flu-dyn cs.PFphysics.comp-ph

Mathis Bode , Damian Alvarez , Paul Fischer , Christos E. Frouzakis , Jens Henrik Göbbert , Joseph A. Insley , Yu-Hsiang Lan , Victor A. Mateevitsi
show 5 more authors
Misun Min Michael E. Papka Silvio Rizzi Roshan J. Samuel Jörg Schumacher
This is my paper · ORCID
classification physics.flu-dyncs.PFphysics.comp-ph PACS 47.27.-i47.55.pb
keywords Rayleigh-BénardconvectiondirectnumericalsimulationGPUcomputingin-situvisualizationturbulentboundarylayersspectralelementmethodNusseltnumberJUWELSBooster
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims to have performed the first direct numerical simulation of Rayleigh-Bénard convection at Rayleigh number $10^{12}$ in a horizontally unconstrained box of aspect ratio $\Gamma=4$, resolving 46.7 billion grid points on 3360 GPUs. Because the thermal and viscous boundary layers fluctuate so rapidly at this Rayleigh number, classical checkpoint-and-visualize post-processing would require moving about 240 TB of data; instead, the authors couple the NekRS solver to the ASCENT in-situ visualization library so images are rendered directly from GPU memory every 100 time steps. The measured cost of that full three-image schedule is a 42.4% overhead, with cheaper reduced schedules at 14.1% and 16.8%. The point is to show that time-resolved visualization of boundary-layer dynamics at extreme Rayleigh numbers is no longer blocked by I/O, and that the resulting data can resolve the plume dynamics that carry heat through the layer.

What carries the argument

The machinery is the coupling of NekRS, a GPU-accelerated spectral-element Navier-Stokes solver, with ASCENT, a lightweight in-situ visualization library that renders from data already resident on the GPU. NekRS uses the spectral element method (SEM): fields are represented as tensor products of order-$p$ polynomials on hexahedral elements, giving $\mathcal{O}(n)$ storage and $\mathcal{O}(np)$ work, and the paper's grids are sized so that at least ten vertical collocation points ($N_{BL}\ge10$) sit inside the thermal boundary layer. The coupling passes device pointers for the mesh, velocity, pressure, and temperature through Conduit, so ASCENT can render the scene without copying data back to the CPU; the resolution criterion and grid-stretching parameters together determine the 46.7-billion-point grid that makes the $Ra=10^{12}$ run feasible.

What would settle it

Perform the $Ra=10^{12}$ case with $N_{BL}=15$ (or at least 12) and compare the horizontally averaged kinetic-energy dissipation profiles and the Nusselt and Reynolds numbers with the present $N_{BL}=10$ run; if the profiles or global transport values differ by more than the statistical error, the claim of a well-resolved DNS at $Ra=10^{12}$ would be overturned.

Watch

Extended reading notes

Core claim

The central claim is in Section 5.5: the NekRS–ASCENT workflow is the first to enable simulations at a very high Rayleigh number of $10^{12}$ with $\Gamma=4$ while capturing the full time-resolved visualization and analysis of the turbulent flow dynamics. The $Ra=10^{12}$ case uses about 46.7 billion spectral-element grid points with $N_{BL}=10$ collocation points inside the thermal boundary layer, runs on 840 nodes with 3360 NVIDIA A100 GPUs, and advances about $2.02\times10^{-4}$ free-fall time units per step without visualization. Rendering the overview scene plus two boundary-layer slices every 100 steps adds 42.4% to the per-step time; reducing the schedule to one boundary-layer slice lowers the overhead to 14.1%. The paper also presents a $\Gamma=4$ database from $Ra=10^5$ to $10^{12}$ with grid resolutions, Nusselt and Reynolds scaling exponents, and frequency spectra showing that boundary-layer fluctuations shift to higher frequencies as $Ra$ increases.

Load-bearing premise

The load-bearing assumption is that ten collocation points inside the thermal boundary layer resolve the flow at $Ra=10^{12}$, even though the $N_{BL}=10$ versus $N_{BL}=15$ convergence test was only performed at $Ra=10^9$.

Editorial extensions

If this is right

  • The 46.7-billion-point $Ra=10^{12}$, $\Gamma=4$ case is reachable on a current GPU cluster at about 0.38 s per time step, establishing a concrete feasibility point for high-$Ra$ convection DNS.
  • Time-resolved in-situ visualization adds a manageable 42.4% overhead for the full three-image schedule, and 14.1% for a single boundary-layer slice, instead of requiring the roughly 240 TB of data that post-processing would need for four free-fall times.
  • The same visualization setup was reproduced on three other NVIDIA-based systems, so the workflow is not tied to one machine.
  • The probe spectra in the thermal boundary layer show fluctuation frequencies increasing with $Ra$, implying that higher-$Ra$ runs must sample at shorter intervals to resolve the dynamics.
  • Using the same $N_{BL}\ge10$ criterion, the paper's memory estimates project the accessible range extending to $Ra=10^{14}$ on the next-generation exascale machine.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the in-situ overhead is dominated by rendering rather than data movement, the authors' numbers suggest that visualization intervals much shorter than 100 steps, for example every 10 or 20 steps, remain affordable; this is an extrapolation, not something the paper measures.
  • The validation gap for $N_{BL}=10$ at $Ra=10^{12}$ could be closed by a companion run at $N_{BL}=15$; such a run requires roughly four times the GPU memory of the present case, which is why the paper leaves it to larger systems.
  • The reported coupling with reactive-flow simulations hints that the same in-situ workflow can be reused for chemically reacting flows, but the paper presents that only as a feasibility test and does not quantify the physics.
  • A practical consequence of in-situ-only visualization is that the choice of scenes and observables must be fixed before the run, since the high-frequency images are never written to disk; this makes exploratory post-hoc visualization impossible for exactly the data that motivated in-situ rendering.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper reports GPU-based direct numerical simulations (DNS) of plane-layer Rayleigh-Bénard convection at Rayleigh numbers up to 10^12 in a periodic domain with aspect ratio Γ = 4, using NekRS on JUWELS Booster (840 nodes / 3360 GPUs). It also presents an ASCENT-based in-situ visualization workflow that renders an overview scene and two boundary-layer slices every 100 time steps, with measured overheads of 42.4% for the full schedule at Ra = 10^12 and 16.8% for a reduced schedule. The authors provide timing and scaling measurements (Tables 4–6, Fig. 13), a grid-resolution criterion NBL ≥ 10, and sample physical results including Nu(Ra) and Re(Ra) scalings, temperature profiles, and velocity spectra. Section 5.5 claims that this is the first Ra = 10^12, Γ = 4 DNS with fully time-resolved visualization and analysis of the turbulent flow dynamics.

Significance. If the resolution criterion is adequate, this is a strong HPC contribution: it demonstrates a zero-copy GPU-to-GPU in-situ pipeline at 90% of JUWELS Booster, carefully quantifies the visualization overhead, and provides a 46.7-billion-point dataset at Ra = 10^12 for a plane-layer convection setup. The timing data in Table 4 and 5 and the scaling data in Figure 13 are internally consistent, and the successful reproduction of the workflow on three additional GPU systems strengthens the portability claim. The physical interpretation advanced via the Nu/Re scalings and boundary-layer fluctuation spectra is plausible but not fully secured: the NBL = 10 resolution criterion is validated only at Ra = 10^9, the Prandtl number is never reported, and the piecewise fits lack quantitative support. These gaps affect the physical portion of the central claim, whereas the workflow/performance demonstration stands as presented.

major comments (3)
  1. [§4.2, Table 4] The resolution criterion NBL ≥ 10 is validated only at Ra = 10^9 in Fig. 3, where the NBL = 10 and NBL = 15 kinetic energy dissipation profiles are described as 'not identical, but they clearly approach each other.' At Ra = 10^12 the production run uses exactly NBL = 10 (Table 4), and no convergence study at this Rayleigh number is provided. Because the authors' own previous work [10] shows that the boundary layers become increasingly fluctuation-dominated as Ra increases, the smallest scales are likely to be smaller at Ra = 10^12 than at Ra = 10^9, so a fixed count of 10 thermal-boundary-layer collocation points does not by itself guarantee a well-resolved DNS. This is load-bearing for the claim in §5.5 that the Ra = 10^12 run captures the full time-resolved DNS dynamics. Please provide additional evidence of resolution sufficiency at Ra = 10^12 (for example, a convergence check against a finer boundary-layer resolution, or a spectral/dissipation-based indicator) or explicitly qualify the physical results as resolution-dependent.
  2. [§4.1, Table 4] The Prandtl number Pr is never stated, although it appears explicitly in the governing equations (1)–(3) and controls the ratio of viscous to thermal boundary-layer thickness. The paper's NBL counts collocation points in the thermal boundary layer, but for Pr > 1 the viscous/kinetic boundary layer is thinner than the thermal one and may contain fewer collocation points. Thus the blanket statement in §4.2 that 'well-resolved DNS of RBC require the boundary layer to be captured with at least 10 collocation points' is incomplete. Please state the Prandtl number used in all runs and report how many collocation points resolve the viscous boundary layer at Ra = 10^12.
  3. [§4.5, Fig. 10] The piecewise power-law fits to Nu(Ra) and Re(Ra) in Fig. 10 are presented without fit coefficients, breakpoints, or uncertainties, and the text notes 'which we took for simplicity here.' The conclusion that the Nu scaling 'is different at moderate and high Ra and in agreement with classical theoretical predictions' is therefore not quantitatively supported by the paper. Please provide the fit parameters and a comparison with the cited theoretical predictions, or reframe these curves as descriptive interpolations rather than quantitative scaling results.
minor comments (5)
  1. [§4.1, Eq. (4)] The dissipation rate ε_U is defined as 2νS:S, but the symbol ν is not defined in the non-dimensional system; please specify that ν = sqrt(Pr/Ra) or give the corresponding dimensional meaning.
  2. [§5.3] The overhead formula reads (tvis + (nvis−1)*tnovis)/(nvis*tnovis), which is the total time ratio rather than the overhead. The quoted percentages (14.1%, 16.8%, 42.4%) correspond to subtracting 1 from this ratio; please correct the formula.
  3. [§5.1, Fig. 13] The text states that for Ra = 10^12 the base point appears to be a negative outlier, causing all other points to show a parallel efficiency above 100%. Parallel efficiency above 100% indicates a baseline artifact; please report the absolute times behind Fig. 13 and clarify how the base point was measured.
  4. [Table 3 and §4.4] The vertical grid stretching parameter is said to vary from 1.3 to 2.0, but the actual value used for each Ra case is not reported. Please provide this information so the grids for the Ra = 10^12 case can be reproduced.
  5. [Fig. 10] The figure caption contains a stray line 'Thumbnail Credits: Wikipedia, NASA' that appears to be an editing artifact and should be removed.

Circularity Check

0 steps flagged · score 0.0 of 10

No material circularity: the workflow and performance claims are independently measured, and the physical scalings are presented as fits, not predictions.

full rationale

The paper's central claims concern the successful coupling of NekRS with ASCENT for high-frequency in-situ visualization at scale, and these are supported by direct measurements: per-time-step timings in Table 4, pipeline cost decompositions in Table 5, scaling data in Figure 13 and Table 6, and GPU-usage monitoring in Figures 14 and 15. None of these quantities is defined in terms of the novelty claim or derived from a self-citation. The Nu and Re results in Section 4.5 and Figure 10 are explicitly power-law fits to the measured data ('the corresponding scaling exponents of piecewise power law fits to the data (which we took for simplicity here)'), so they are not fitted inputs dressed up as predictions. The NBL >= 10 resolution criterion is calibrated in Section 4.2 by a convergence comparison of kinetic-energy dissipation at Ra = 1e9, following Scheel et al. [34], and then applied at higher Rayleigh numbers; this is an extrapolation and a resolution-assumption risk, but it is not circular reasoning because the criterion is not obtained from the Ra = 1e12 results it is used to validate. Self-citations appear, notably [10] for the fluctuation-dominated boundary-layer picture and [13] for JUPITER benchmark membership, but neither carries a derivation: the fluctuation argument is independently supported by the paper's own probe spectra in Figure 11, and the benchmark statement is a factual claim rather than an inference. No equation is defined in terms of its own output, and no derived quantity reduces by construction to an input of the same calculation. The strongest caveat, that the 'first Ra = 1e12 DNS' claim rests on an NBL = 10 resolution choice validated only at lower Rayleigh number with Pr unreported, is a correctness or verification concern, not a circularity concern, so it does not affect the circularity score.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical particles, forces, or fields. Its novel components are a computational workflow and a dataset, which are not invented physical entities. The main unfitted input parameters are the Rayleigh number (scanned), the Prandtl number (omitted), and the grid stretching values (chosen by hand).

free parameters (3)
  • Prandtl number Pr = not stated
    Governing dimensionless parameter in Eqs. (1) and (2); never specified anywhere in the text or tables, yet every simulated case and the Nu and Re results depend on its value.
  • Vertical grid stretching parameter = 1.3 to 2.0
    Chosen by hand per Rayleigh number case to concentrate collocation points in the thermal boundary layer; directly influences NBL and the reported resolution.
  • Piecewise Nu-Ra and Re-Ra scaling exponents = not reported in text
    Exponents obtained from piecewise power-law fits to the simulation data in Figure 10; used to characterize heat and momentum transport scaling with no error bars or comparison to prior DNS.
assumptions (5)
  • domain assumption The Boussinesq approximation is valid for the simulated convection layer.
    Eqs. (1)-(3) use the Boussinesq form with a T z buoyancy term; standard for small temperature differences, but no parameter range or justification is given.
  • domain assumption NBL >= 10 collocation points in the thermal boundary layer is sufficient for converged statistics at all Ra up to 10^12.
    Section 4.2 and Figure 3 validate NBL=10 against NBL=15 only at Ra=10^9; no convergence test is shown at Ra=10^12.
  • domain assumption Aspect ratio Gamma=4 approximates horizontally unbounded plane-layer convection.
    Section 4.3 shows converging thermal boundary layer heights between Gamma=4 and 8 for Ra<=10^9, but the kinetic boundary layer heights do not clearly converge; the Gamma=4 choice is extrapolated to higher Ra.
  • domain assumption Statistically steady state is reached after roughly 24 hours of simulation, and the listed averaging times are sufficient.
    Table 4 lists averaging times in free-fall units but provides no convergence diagnostics or sensitivity checks for the statistics.
  • standard math The spectral element discretization with GLL quadrature accurately solves the incompressible Navier-Stokes and temperature equations.
    NekRS's spectral element method is a standard, well-tested discretization; the paper relies on it without presenting a code validation against an independent benchmark.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow." pith.science (2026). https://pith.science/paper/JT47RYJV

@misc{pith2026250113240,
  author       = {Pith},
  title        = {Pith review of: Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JT47RYJV}},
  note         = {Machine review of arXiv:2501.13240}
}
abstract

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-B\'enard 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.

Figures

Figures reproduced from arXiv: 2501.13240 by the authors.

Figure 1
Figure 1. JUWELS Booster node architecture. GPU has an HCA to an HDR InfiniBand fabric. The injection bandwidth per node is 800 Gbps. The InfiniBand fabric in JUWELS is the result of integrating 3 different fabrics: A Fat Tree on the JUWELS Cluster mod￾ule, a small Fat Tree as part of a flash storage based on IME, and a DragonFly+ fabric with adaptive routing on the JUWELS Booster module. The JUWELS Booster module contains a … view at source ↗
Figure 2
Figure 2. Schematic of Rayleigh-Bénard convection setup. The top plate is held [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Kinetic energy dissipation as a function of the distance from the bot [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Accessible configurations for different supercomputers for NBL = 10 (top) and NBL = 15 (bottom). All estimates assume p = 7. height for Γ = 2 is lowest for all Ra and, in particular, is fur￾ther away from the resulting boundary layer heights for Γ = 4 and Γ = 8 than th…
Figure 5
Figure 5. Figure 5: Aspect ratio-dependence of RMS profiles of temperature [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 7
Figure 7. Figure 7: Overview scenes for all Rayleigh number cases with [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Temperature contours in the top (top row) and bottom (bottom row) boundary layers for [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Temperature profiles for 105 ≤ Ra ≤ 1012 and Γ = 4. The left inset shows a close-up of the profiles near the bottom plate for the five highest values of Ra. The right inset shows the root-mean-square profiles of T, near the bottom wall for the three highest Ra. eter, w…
Figure 11
Figure 11. Figure 11: Frequency spectra of velocity measured by a probe in the thermal [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Sequence of boundary layer snapshots at Ra = 1012 written out each 100 time steps (from left to right). Fully resolved videos for the top and bottom boundary layer are added in the Supplemental Material of this article [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: Strong scaling of NekRS on JUWELS Booster for RBC at [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]
Figure 14
Figure 14. Figure 14: GPU usage of the part of the large-scale simulation at [PITH_FULL_IMAGE:figures/full_fig_p013_14.png]
Figure 15
Figure 15. Figure 15: GPU memory usage for a simulation without (top) and with in-situ [PITH_FULL_IMAGE:figures/full_fig_p014_15.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 31 canonical work pages

  1. [10]

    R. J. Samuel, M. Bode, J. D. Scheel, K. R. Sreenivasan, J. Schumacher, No sustained mean velocity in the boundary region of plane thermal con- vection, Journal of Fluid Mechanics 996 (2024) A49. doi:10.1017/ jfm.2024.853

    work page 2024

  2. [1]

    J. D. Anderson, Computational Fluid Dynamics: The Basics With Appli- cations, McGraw-Hill, New York, 1995

    work page 1995

  3. [2]

    P. K. Yeung, K. Ravikumar, Advancing understanding of turbulence through extreme-scale computation: Intermittency and simulations at large problem sizes, Phys. Rev. Fluids 5 (2020) 110517. doi:10.1103/ PhysRevFluids.5.110517

    work page 2020

  4. [3]

    M. K. Verma, R. J. Samuel, S. Chatterjee, S. Bhattacharya, A. Asad, Chal- lenges in fluid flow simulations using exascale computing, S.N. Comput. Sci. 1 (178) (2020) 178

    work page 2020

  5. [4]

    Trebotich, Exascale Computational Fluid Dynamics in Heterogeneous Systems, Journal of Fluids Engineering 146 (4) (2024) 041104

    D. Trebotich, Exascale Computational Fluid Dynamics in Heterogeneous Systems, Journal of Fluids Engineering 146 (4) (2024) 041104. doi: 10.1115/1.4064534

    work page doi:10.1115/1.4064534 2024

  6. [5]

    P. F. Fischer, S. Kerkemeier, M. Min, Y .-H. Lan, M. Phillips, T. Rath- nayake, E. Merzari, A. Tomboulides, A. Karakus, N. Chalmers, T. War- burton, NekRS, a GPU-accelerated spectral element Navier–Stokes solver, Parallel Comput. 114 (2022) 102982

    work page 2022

  7. [6]

    Merzari, S

    E. Merzari, S. Hamilton, T. Evans, M. Min, P. Fischer, S. Kerkemeier, J. Fang, P. Romano, Y .-H. Lan, M. Phillips, E. Biondo, K. Royston, T. Warburton, N. Chalmers, T. Rathnayake, Exascale multiphysics nu- clear reactor simulations for advanced designs, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and...

    arXiv 2023

  8. [7]

    Strohmaier, H

    E. Strohmaier, H. Meuer, J. Dongarra, H. Simon, The top500 list and progress in high-performance computing, Computer 48 (2015) 42–49. doi:10.1109/MC.2015.338. 15

    work page doi:10.1109/mc.2015.338 2015

Show all 38 references
  1. [8]

    Ahlers, S

    G. Ahlers, S. Grossmann, D. Lohse, Heat transfer and large scale dy- namics in turbulent Rayleigh-Bénard convection, Rev. Mod. Phys. 81 (2) (2009) 503–537

    2009

  2. [9]

    Chillà, J

    F. Chillà, J. Schumacher, New perspectives in turbulent Rayleigh-Bénard convection, Eur. Phys. J. E 35 (7) (2012) 58

    2012

  3. [11]

    Larsen, J

    M. Larsen, J. Ahrens, U. Ayachit, E. Brugger, H. Childs, B. Geveci, C. Harrison, The alpine in situ infrastructure: Ascending from the ashes of strawman, in: Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization, 2017, pp. 42–46

    2017

  4. [12]

    Larsen, E

    M. Larsen, E. Brugger, H. Childs, C. Harrison, Ascent: A Flyweight In Situ Library for Exascale Simulations, in: H. Childs, J. C. Bennett, C. Garth (Eds.), In Situ Visualization for Computational Science, Mathe- matics and Visualization, Springer International Publishing, Cham...

    2022 doi

  5. [13]

    Herten, S

    A. Herten, S. Achilles, D. Alvarez, J. Badwaik, E. Behle, M. Bode, T. Breuer, D. Caviedes-V oullième, M. Cherti, A. Dabah, S. El Sayed, W. Frings, A. Gonzalez-Nicolas, E. B. Gregory, K. H. Mood, T. Hater, J. Jitsev, C. M. John, J. H. Meinke, C. I. Meyer, P. Mezentsev, J.-O. Mi...

    2024 arXiv

  6. [14]

    Jülich Supercomputing Centre, JUWELS Cluster and Booster: Exascale Pathfinder with Modular Supercomputing Architecture at Juelich Super- computing Centre, Journal of large-scale research facilities 7 (2021)

    2021

  7. [15]

    A. T. Patera, A spectral element method for fluid dynamics: laminar flow in a channel expansion, Journal of computational Physics 54 (3) (1984) 468–488

    1984

  8. [16]

    M. O. Deville, P. F. Fischer, E. H. Mund, High-order methods for incom- pressible fluid flow, V ol. 9, Cambridge university press, 2002

    2002

  9. [17]

    S. A. Orszag, Spectral methods for problems in complex geometries, Journal of Computational Physics 37 (1) (1980) 70–92. doi:https: //doi.org/10.1016/0021-9991(80)90005-4

    1980 doi

  10. [18]

    Min, Y .-H

    M. Min, Y .-H. Lan, P. Fischer, E. Merzari, S. Kerkemeier, M. Phillips, T. Rathnayake, A. Novak, D. Gaston, N. Chalmers, et al., Optimization of full-core reactor simulations on summit, in: SC22: International Confer- ence for High Performance Computing, Networking, Storage an...

    2022

  11. [19]

    Min, Y .-H

    M. Min, Y .-H. Lan, P. Fischer, T. Rathnayake, J. Holmen, Nek5000/rs per- formance on advanced gpu architectures, Frontiers in High Performance Computing (submitted 2023)

    2023

  12. [20]

    Phillips, Spectral element poisson preconditioners for heterogeneous architectures, Ph.D

    M. Phillips, Spectral element poisson preconditioners for heterogeneous architectures, Ph.D. thesis, University of Illinois at Urbana-Champaign (2023)

    2023

  13. [21]

    D. S. Medina, A. St-Cyr, T. Warburton, Occa: A unified approach to multi-threading languages, arXiv preprint arXiv:1403.0968 (2014)

    2014 arXiv

  14. [22]

    Medina, Okl: A unified language for parallel architectures, Ph.D

    D. Medina, Okl: A unified language for parallel architectures, Ph.D. the- sis, Rice University Doctoral Thesis (2015)

    2015

  15. [23]

    Ayachit, A

    U. Ayachit, A. C. Bauer, B. Boeckel, B. Geveci, K. Moreland, P. O’Leary, T. Osika, Catalyst revised: Rethinking the paraview in situ analysis and visualization api, in: H. Jagode, H. Anzt, H. Ltaief, P. Luszczek (Eds.), High Performance Computing, Springer International Publis...

    2021 doi

  16. [24]

    Kuhlen, R

    T. Kuhlen, R. Pajarola, K. Zhou, Parallel in situ coupling of simulation with a fully featured visualization system, in: Proceedings of the 11th Eu- rographics Conference on Parallel Graphics and Visualization (EGPGV), V ol. 10, Eurographics Association Aire-la-Ville, Switzerl...

    2011

  17. [25]

    Childs, In Situ Processing (Nov

    H. Childs, In Situ Processing (Nov. 2012). URL https://escholarship.org/uc/item/3st8x19d

    2012

  18. [26]

    Ayachit, B

    U. Ayachit, B. Whitlock, M. Wolf, B. Loring, B. Geveci, D. Lonie, E. W. Bethel, The SENSEI Generic In Situ Interface, in: 2016 Second Work- shop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISA V), 2016, pp. 40–44.doi:10.1109/ISAV.2016.013

    2016 doi

  19. [27]

    Messina, The Exascale Computing Project, Computing in Science & Engineering 19 (3) (2017) 63–67, conference Name: Computing in Sci- ence & Engineering

    P. Messina, The Exascale Computing Project, Computing in Science & Engineering 19 (3) (2017) 63–67, conference Name: Computing in Sci- ence & Engineering. doi:10.1109/MCSE.2017.57

    2017 doi

  20. [28]

    Moreland, C

    K. Moreland, C. Sewell, W. Usher, L.-t. Lo, J. Meredith, D. Pugmire, J. Kress, H. Schroots, K.-L. Ma, H. Childs, M. Larsen, C.-M. Chen, R. Maynard, B. Geveci, VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures, IEEE Computer Graphics and Applica...

    2016 doi

  21. [29]

    Childs, VisIt: An End-User Tool for Visualizing and Analyzing Very Large Data (Nov

    H. Childs, VisIt: An End-User Tool for Visualizing and Analyzing Very Large Data (Nov. 2012). URL https://escholarship.org/uc/item/69r5m58v

    2012

  22. [30]

    V . A. Mateevitsi, M. Bode, N. Ferrier, P. Fischer, J. H. Göbbert, J. A. Insley, Y .-H. Lan, M. Min, M. E. Papka, S. Patel, et al., Scaling com- putational fluid dynamics: In situ visualization of nekrs using sensei, in: Proceedings of the SC’23 Workshops of The International ...

    2023

  23. [31]

    Harrison, M

    C. Harrison, M. Larsen, B. S. Ryujin, A. Kunen, A. Capps, J. Privitera, Conduit: A successful strategy for describing and sharing data in situ, in: 2022 IEEE/ACM International Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISA V), IE...

    2022

  24. [32]

    A. V . Getling, Rayleigh-Bénard Convection: Structures and Dynamics, World Scientific, Singapore, 1998

    1998

  25. [33]

    M. K. Verma, Physics of Buoyant Flows: From Instabilities to Turbu- lence, World Scientific, Singapore, 2018

    2018

  26. [34]

    J. D. Scheel, M. S. Emran, J. Schumacher, Resolving the fine-scale struc- ture in turbulent rayleigh–bénard convection, New Journal of Physics 15 (2013) 113063

    2013

  27. [35]

    W. V . R. Malkus, The Heat Transport and Spectrum of Thermal Turbu- lence, Proceedings of the Royal Society of London. Series A 225 (1) (1954) 196–212

    1954

  28. [36]

    E. A. Spiegel, Convection in stars: I. Basic Boussinesq convection, Annu. Rev. Astron. Astrophys. 9 (1971) 323–352

    1971

  29. [37]

    Witzler, F

    C. Witzler, F. S. M. Guimarães, D. Mira, H. Anzt, J. H. Göbbert, W. Frings, M. Bode, JuMonC: A RESTful tool for enabling monitoring and control of simulations at scale, Future Generation Computer Systems 164 (2025) 107541. doi:https://doi.org/10.1016/j.future. 2024.107541

    2025

  30. [38]

    Kerkemeier, C

    S. Kerkemeier, C. E. Frouzakis, A. G. Tomboulides, P. Fischer, M. Bode, nekcrf: A next generation high-order reactive low mach flow solver for direct numerical simulations, arXiv preprint arXiv:2409.06404 (2024). 16

    2024 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.