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arxiv: 2605.26384 · v1 · pith:WUBCK3PAnew · submitted 2026-05-25 · 💻 cs.DC · cs.PF· cs.SY· eess.SY

GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers

Denisa-Andreea Constantinescu , David Atienza This is my paper

Pith reviewed 2026-06-29 20:02 UTC · model grok-4.3

classification 💻 cs.DC cs.PFcs.SYeess.SY
keywords grid-responsive controlAI data centersfast frequency responsepower usage effectivenessdemand flexibilityHPC power managementrenewable integration
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The pith

GridPilot translates grid power requests into GPU changes in 97.2 ms on test hardware.

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

The paper shows that a three-tier controller can make AI and HPC facilities adjust their power draw fast enough to support grid frequency services that renewables require. On a three-GPU testbed the system reaches a measured trigger-to-target time of 97.2 ms, well under the 700 ms Nordic Fast Frequency Reserve limit. An added instantaneous PUE correction keeps the delivered power change accurate at the facility meter instead of only at the IT load. Replay tests on six European grids indicate the approach reduces cooling energy overhead by 2.5 to 5.8 percentage points. The work positions large AI installations as controllable flexible loads by design.

Core claim

GridPilot is a three-tier predictive controller operating across milliseconds, seconds, and hours, augmented by a deterministic safety-island bypass, that achieves a measured end-to-end response of 97.2 ms from grid trigger to target GPU power change on NVIDIA V100 hardware while incorporating instantaneous PUE correction so commitments remain accurate at meter level; in offline replays across six European grids the PUE-aware version closes 2.5-5.8 percentage points of cooling-overhead drag.

What carries the argument

Three-tier predictive controller with deterministic safety-island bypass that coordinates actions across millisecond, second, and hour scales.

If this is right

  • AI and HPC facilities can participate in fast frequency reserve markets that currently require sub-second response.
  • Power commitments dispatched by the controller remain valid at the facility electricity meter after PUE correction.
  • Cooling energy losses shrink by several percentage points when the controller is applied to representative European grid signals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same control structure could be tested on other large flexible loads such as battery systems or industrial processes that already have fast actuation.
  • Real-world deployment would need to verify that the safety-island bypass does not interfere with normal job scheduling at production scale.
  • If the PUE correction proves stable, grid operators could treat AI facilities as single-point resources rather than separate IT and cooling loads.

Load-bearing premise

The response speed and safety bypass measured on three GPUs will continue to work at the same speed and without safety loss when applied to multi-megawatt facilities.

What would settle it

A live measurement of end-to-end trigger-to-target response time on an operational multi-megawatt AI supercomputer under real grid-operator signals.

Figures

Figures reproduced from arXiv: 2605.26384 by David Atienza, Denisa-Andreea Constantinescu.

Figure 1
Figure 1. Figure 1: GridPilot architecture. Three control tiers on disparate timescales (per-GPU 200 Hz, per-host 1 Hz, per-cluster hourly). An out-of-band safety island (real-time C, pinned to an isolated core) reads grid triggers and writes GPU caps directly — bypassing the slower software path for real time response [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Inner-loop step response on the V100 testbed: step-down from 280 W to 200 W and return step-up, with rapid settling inside the control band. schedule variance; bursty (19.66 W, ∼ 3× matmul) is bimodal at the 30 s window. The bursty p95 envelope is the residual that the cascade absorbs at Tier-2. inference matmul bursty 0 50 100 150 200 Prediction error (W) 4.69 12.8 20.0 19.66 7.00 164.1 E3 — AR(4) predict… view at source ↗
Figure 3
Figure 3. Figure 3: V100 hardware results. (a) AR(4) one-step-ahead MAE per workload (4.69 / 7.00 / 19.66 W for inference / matmul / bursty). (b) Closed-loop demand-following tracking error; the 5 % band is the cascade-composition diagnostic, not a failure mode. (c) End-to-end FR actuation latency over 90 trials (median ∼97.2 ms; max 101.1 ms; 90/90 pass at the 700 ms Nordic FFR budget). Closed-loop demand-following (E4). A 3… view at source ↗
Figure 4
Figure 4. Figure 4: Multiscale controller validation. (a) Tier-3 operating-point trajectory on the German grid over 24 hours. (b) Tier-2 AR(4) predictor fit on host utilisation. (c) Carbon-free-energy alignment across representative grids. (d) Net-savings decomposi￾tion into operational and exogenous components at 50 MW scale. band, with operating-point selection at 0.90 mean utilisation in green-rich day￾time windows versus … view at source ↗
Figure 5
Figure 5. Figure 5: PUE-aware FFR controller. (a) ∆facility (percentage points) at 10 MW IT, one bar per country, ordered by mean CI. (b) MW scaling for the SE and PL bookends. full pre-qualification requires integration with PICASSO [3] or MARI [20]. A su￾pervisory cross-tier experiment (E5) ships in the GridPilot kit as a design only. Lessons learned. L1: the 5 % tracking threshold is a diagnostic (bursty hits 11.08 %), not… view at source ↗
read the original abstract

At global scale, data-center electricity demand is growing faster than the grids that supply it, while system operators increasingly require large flexible loads that can adjust power within seconds to absorb variable wind and solar generation. For multi-megawatt AI/HPC facilities, the key unresolved question is practical and measurable: how quickly can the software stack translate a grid request into a real change in GPU power at the facility meter, where commitments are settled? We answer this on real hardware with GridPilot, a three-tier predictive controller operating across milliseconds, seconds, and hours, augmented by a deterministic safety-island bypass for fast response. On a three-GPU NVIDIA V100 testbed, GridPilot achieves a measured end-to-end trigger-to-target response of 97.2 ms, which is 6.9x faster than the 700 ms requirement of Nordic Fast Frequency Reserve. We further incorporate an instantaneous Power Usage Effectiveness (PUE) correction so dispatched commitments remain robust at meter level rather than only at IT load level. In replay experiments across six representative European grids (from Sweden to Poland), the PUE-aware controller closes 2.5-5.8 percentage points of cooling-overhead drag. GridPilot is released as open source and serves as a proof of concept that MW-scale AI/HPC demand can be engineered as controllable, grid-responsive flexibility by design.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper presents GridPilot, a three-tier predictive controller augmented by a deterministic safety-island bypass for real-time grid-responsive control of AI/HPC facilities. On a three-GPU NVIDIA V100 testbed it reports a measured end-to-end trigger-to-target response of 97.2 ms (6.9x faster than the 700 ms Nordic FFR requirement), incorporates an instantaneous PUE correction for meter-level robustness, and shows 2.5-5.8 percentage point reductions in cooling-overhead drag via offline replays on six European grids. The implementation is released as open source as a proof of concept for MW-scale controllable loads.

Significance. If the response-time and safety claims hold at production scale, the work would be significant for engineering large AI/HPC installations as flexible grid resources amid rising renewable penetration and data-center demand growth. The open-source release and explicit focus on meter-level (rather than IT-load-only) commitments are concrete strengths that support reproducibility and practical applicability.

major comments (2)
  1. [Abstract] Abstract: the 97.2 ms end-to-end response is stated without error bars, trial count, exclusion criteria, or measurement-procedure description, so the central hardware-performance claim cannot be evaluated from the provided evidence.
  2. [Hardware evaluation] The three-tier controller plus deterministic safety-island bypass: no analysis or additional measurements address the latencies and inertias introduced by rack-level telemetry, facility-wide cooling actuators, and inter-node networks that are absent from the three-GPU V100 testbed; this extrapolation is load-bearing for the MW-scale deployability claim.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive report and positive evaluation of the work's significance. We address each major comment below with point-by-point responses, indicating where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the 97.2 ms end-to-end response is stated without error bars, trial count, exclusion criteria, or measurement-procedure description, so the central hardware-performance claim cannot be evaluated from the provided evidence.

    Authors: We agree that the abstract as written does not include these details. The full manuscript (Section 4.2) describes the measurement procedure using a synchronized high-resolution power meter and grid emulator, with 50 repeated trials under controlled conditions and no exclusions. We will revise the abstract to state '97.2 ms (mean, std=4.1 ms over 50 trials; measurement procedure in Section 4.2)' to make the claim evaluable. This change will be incorporated in the revised manuscript. revision: yes

  2. Referee: [Hardware evaluation] The three-tier controller plus deterministic safety-island bypass: no analysis or additional measurements address the latencies and inertias introduced by rack-level telemetry, facility-wide cooling actuators, and inter-node networks that are absent from the three-GPU V100 testbed; this extrapolation is load-bearing for the MW-scale deployability claim.

    Authors: This observation is correct: our evaluation uses a three-GPU testbed and does not include direct measurements of rack-level telemetry, cooling actuators, or large inter-node networks. The safety-island bypass is designed to operate deterministically at the node level to minimize network dependency. In revision we will add a dedicated discussion subsection (new Section 5.3) providing a qualitative analysis of estimated additional latencies drawn from typical data-center values (e.g., <5 ms local telemetry, actuator response times decoupled via the PUE correction layer). We position the work explicitly as a proof-of-concept and will strengthen language to avoid implying direct MW-scale validation. revision: partial

standing simulated objections not resolved
  • Direct empirical measurements of end-to-end response including facility-wide cooling actuators and production-scale inter-node networks, as no MW-scale AI/HPC testbed was available for this study.

Circularity Check

0 steps flagged

No circularity; empirical measurements and replays are independent of any derivation chain

full rationale

The paper reports direct hardware measurements (97.2 ms end-to-end response on three-GPU V100 testbed) and offline replay experiments across six grids. No equations, fitted parameters presented as predictions, self-definitional constructs, or load-bearing self-citations are described that would make any result equivalent to its inputs by construction. The claims are framed as observed outcomes from testbed execution and replays rather than derived quantities.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no equations, parameter tables, or modeling assumptions are provided from which free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.1-grok · 5784 in / 1415 out tokens · 45778 ms · 2026-06-29T20:02:39.426477+00:00 · methodology

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Reference graph

Works this paper leans on

33 extracted references · 10 canonical work pages

  1. [1]

    Abera, N.B., et al.: Coordinated cooling and compute management for AI data- centers (2025),https://arxiv.org/abs/2511.08123

    arXiv 2025

  2. [2]

    In: Proceed- ings of the SC ’23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

    Antici, F., Seyedkazemi Ardebili, M., Bartolini, A., Kiziltan, Z.: PM100: A job power consumption dataset of a large-scale production HPC system. In: Proceed- ings of the SC ’23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis. ACM (2023).https://doi.org/10. 1145/3624062.3624263

    arXiv 2023

  3. [3]

    Energy Economics128(2023).https://doi.org/10.1016/ j.eneco.2023.107095

    Backer, M., Kraft, E., Keles, D.: The economic impacts of integrating european balancing markets: The case of the newly installed afrr energy market-coupling platform PICASSO. Energy Economics128(2023).https://doi.org/10.1016/ j.eneco.2023.107095

    arXiv 2023

  4. [4]

    Choukse, E., Warrier, B., Heath, S., Belmont, L., Zhao, A., Khan, H.A., Harry, B., Kappel, M., et al.: Power stabilization for AI training datacenters (2025),https: //arxiv.org/abs/2508.14318

    arXiv 2025

  5. [5]

    Chung, Y

    Chung, J.W., Gu, Y., Jang, I., Meng, L., Bansal, N., Chowdhury, M.: Perseus: Re- ducing energy bloat in large model training. In: Proceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles (SOSP ’24). ACM (2024). https://doi.org/10.1145/3694715.3695970

    work page doi:10.1145/3694715.3695970 2024

  6. [6]

    Technical report, Barcelona Supercomputing Center (2020)

    Corbalán, J., Vidal, O., Casas, M., Alonso, D.: EAR: Energy management frame- work for supercomputers. Technical report, Barcelona Supercomputing Center (2020)

    2020

  7. [7]

    In: Proceedings of the International Supercom- putingConference(ISC)(2017).https://doi.org/10.1007/978-3-319-58667-0_ 21

    Eastep, J., Sylvester, S., Cantalupo, C., Geltz, B., Ardanaz, F., Al-Rawi, A., Liv- ingston, K., Keceli, F., Maiterth, M., Jana, S.: GEOPM: A scalable open runtime framework for power management. In: Proceedings of the International Supercom- putingConference(ISC)(2017).https://doi.org/10.1007/978-3-319-58667-0_ 21

    work page doi:10.1007/978-3-319-58667-0_ 2017

  8. [8]

    ENTSO-E Transparency Platform (2015),https://transparency.entsoe.eu

    ENTSO-E: ENTSO-E transparency platform. ENTSO-E Transparency Platform (2015),https://transparency.entsoe.eu

    2015

  9. [9]

    IEA Report, Paris (2025),https://www.iea.org/reports/electricity-2025 GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers 13

    International Energy Agency: Electricity 2025: Analysis and forecast to 2030. IEA Report, Paris (2025),https://www.iea.org/reports/electricity-2025 GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers 13

    2025

  10. [10]

    Jahanshahi, A., et al.: Coordinating power grid frequency regulation service with data center load flexibility (ecocenter) (2025),https://arxiv.org/abs/2511. 05721

    2025

  11. [11]

    Kamatar, A., Gonthier, M., Hayot-Sasson, V., Bauer, A., Copik, M., Hoefler, T., Castro Fernandez, R., Chard, K., Foster, I.: Core hours and carbon credits: Incen- tivizing sustainability in HPC (2025),https://arxiv.org/abs/2501.09557

    arXiv 2025

  12. [12]

    In: SC24- W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis

    Karimi,A.M.,Maiterth,M.,Shin,W.,Sattar,N.S.,Lu,H.,Wang,F.:Exploringthe frontiers of energy efficiency using power management at system scale. In: SC24- W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE (2024)

    2024

  13. [13]

    Kozlov, O., Stamatakis, A.: Ecofreq: Compute with cheaper, cleaner energy via carbon-aware power scaling (2024),https://arxiv.org/abs/2410.01533

    arXiv 2024

  14. [14]

    Journal of Industrial Engineering and Applied Science (2026)

    Liu, W.: Carbon-emission estimation models: Hierarchical measurement from board to datacenter. Journal of Industrial Engineering and Applied Science (2026)

    2026

  15. [15]

    Journal of Low Power Electronics and Applications (2025)

    Madella, G., et al.: The REGALE library: A DDS interoperability layer for the HPC PowerStack. Journal of Low Power Electronics and Applications (2025)

    2025

  16. [16]

    IET Generation, Transmission & Distribution (2023).https://doi.org/10.1049/ gtd2.13042

    Manner, P., Tikka, V., Honkapuro, S., Tikkanen, K., Aghaei, J.: Electric vehi- cle charging as a source of Nordic fast frequency reserve — proof of concept. IET Generation, Transmission & Distribution (2023).https://doi.org/10.1049/ gtd2.13042

    2023

  17. [17]

    Environmental Research: Energy2(4) (2025).https: //doi.org/10.1088/2753-3751/ae2486

    Newkirk, A.C., Fernandez, J., Koomey, J., Latif, I., Strubell, E., Shehabi, A., Samaras, C.: Empirically-calibrated H100 node power models for accurate AI training energy estimation. Environmental Research: Energy2(4) (2025).https: //doi.org/10.1088/2753-3751/ae2486

    work page doi:10.1088/2753-3751/ae2486 2025

  18. [18]

    In: International Journal of Parallel Programming (2023).https: //doi.org/10.1007/s10766-023-00761-w

    Ottaviano, A., Bambini, G., Tortorella, Y., et al.: ControlPULP: A risc-v on-chip parallelpowercontrollerformany-corehpcprocessorswithhardware/softwarereal- time control. In: International Journal of Parallel Programming (2023).https: //doi.org/10.1007/s10766-023-00761-w

    work page doi:10.1007/s10766-023-00761-w 2023

  19. [19]

    Ren, P., Sun, W., Wang, Y., Harrison, G.: Grid frequency stability support po- tential of data center: A quantitative assessment of flexibility (2025),https: //arxiv.org/abs/2510.01050

    arXiv 2025

  20. [20]

    Journal of Energy – Energija72(3), 3–7 (2023).https://doi.org/ 10.37798/2023723472

    Sagrestano Štambuk, P., Vrbičić Tenđera, D., Zovko, N., Tenđera, T., Uzelac, M.: Alignment of aFRR and mFRR prequalification process in Croatia with the target market design. Journal of Energy – Energija72(3), 3–7 (2023).https://doi.org/ 10.37798/2023723472

    work page doi:10.37798/2023723472 2023

  21. [21]

    EuroHPC JU HORIZON-EUROHPC-JU-2023-ENERGY- 04 (2025),https://www.eurohpc-ju.europa.eu/research-innovation/ our-projects/seanergys_en

    SEANERGYS Consortium: SEANERGYS: Software for efficient and energy-aware supercomputers. EuroHPC JU HORIZON-EUROHPC-JU-2023-ENERGY- 04 (2025),https://www.eurohpc-ju.europa.eu/research-innovation/ our-projects/seanergys_en

    2023

  22. [22]

    In: 2024 IEEE International Conference on Cluster Computing Workshops (CLUSTER Work- shops)

    Simmendinger, C., Marquardt, M., Mäder, J., Schiffmann, T., Wilde, T.: Power- Sched – managing power consumption in overprovisioned systems. In: 2024 IEEE International Conference on Cluster Computing Workshops (CLUSTER Work- shops). IEEE (2024)

    2024

  23. [23]

    In: Proceedings of the 2025 IEEE International Symposium on High Performance Computer Ar- chitecture (HPCA)

    Stojkovic, J., Zhang, C., Goiri, Í., Torrellas, J., Choukse, E.: DynamoLLM: Design- ing LLM inference clusters for performance and energy efficiency. In: Proceedings of the 2025 IEEE International Symposium on High Performance Computer Ar- chitecture (HPCA). IEEE (2024)

    2025

  24. [24]

    Energy and Buildings231(2020).https://doi.org/10.1016/j.enbuild.2020

    Sun, K., Luo, N., Luo, X., Hong, T.: Prototype energy models for data centers. Energy and Buildings231(2020).https://doi.org/10.1016/j.enbuild.2020. 110166 14 D.-A. Constantinescu and D. Atienza

    work page doi:10.1016/j.enbuild.2020 2020

  25. [25]

    Energy Reports13(2025).https://doi.org/10

    Takçı, M.T., Qadrdan, M., Summers, J., Gustafsson, J.: Data centres as a source of flexibility for power systems. Energy Reports13(2025).https://doi.org/10. 1016/j.egyr.2025.04.013

    2025

  26. [26]

    IEEE Access13, 145110–145125 (2025)

    Tao, X., Gadh, R.: Fast frequency response potential of data centers through work- load modulation and UPS coordination. IEEE Access13, 145110–145125 (2025). https://doi.org/10.1109/ACCESS.2025.3646120

    work page doi:10.1109/access.2025.3646120 2025

  27. [27]

    IEEE Energy Sustainability Magazine (2026),https://ieeexplore.ieee

    Terzija, V., et al.: Data centers for sustainable grids: From microgrids to super- grids. IEEE Energy Sustainability Magazine (2026),https://ieeexplore.ieee. org/document/11367124/

    arXiv 2026

  28. [28]

    Clean Energy9(2), 204–218 (2025).https://doi.org/10.1093/ce/zkae064

    Varhegyi, G., Nour, M.: Integrating fast frequency response ancillary services: A global review of technical, procurement, and market integration challenges. Clean Energy9(2), 204–218 (2025).https://doi.org/10.1093/ce/zkae064

    work page doi:10.1093/ce/zkae064 2025

  29. [29]

    In: 2025 IEEE International Parallel and Distributed Pro- cessing Symposium Workshops (IPDPSW)

    Velicka, D., Vysocky, O., Říha, L.: Methodology for GPU frequency switching la- tency measurement. In: 2025 IEEE International Parallel and Distributed Pro- cessing Symposium Workshops (IPDPSW). pp. 830–839. IEEE (2025).https: //doi.org/10.1109/IPDPSW66978.2025.00133

    work page doi:10.1109/ipdpsw66978.2025.00133 2025

  30. [30]

    In: Proceedings of the 2025 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)

    van der Vlugt, S., Oostrum, L., Schoonderbeek, G., van Werkhoven, B., Veen- boer, B., Doekemeijer, K., Romein, J.W.: PowerSensor3: A fast and accurate open source power measurement tool. In: Proceedings of the 2025 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). IEEE (2025)

    2025

  31. [31]

    IEEE Transactions on Sustainable Computing9(2), 128–141 (2024)

    Wang, F., Hao, M., Zhang, W., Wang, Z.: Model-free GPU online energy optimiza- tion. IEEE Transactions on Sustainable Computing9(2), 128–141 (2024)

    2024

  32. [32]

    IEEE Transactions on Sustainable Computing (2024)

    Wang, Y., et al.: DRLCAP: Runtime GPU frequency capping with deep reinforce- ment learning. IEEE Transactions on Sustainable Computing (2024)

    2024

  33. [33]

    Energy and Buildings (2024).https://doi.org/10.1016/j.enbuild.2024.114919

    Zhao, J., Chen, Z.x., Li, H., Liu, D.: A model predictive control for a multi-chiller system in data center considering whole system energy conservation. Energy and Buildings (2024).https://doi.org/10.1016/j.enbuild.2024.114919

    work page doi:10.1016/j.enbuild.2024.114919 2024