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arxiv: 2606.25082 · v1 · pith:FGPUFQP6new · submitted 2026-06-23 · 💻 cs.DC

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning

Ellie Lipe , Neel Karia , Connor Espenshade , Clifford Stein , Asser Tantawi , Olivier Tardieu This is my paper

Pith reviewed 2026-06-25 22:31 UTC · model grok-4.3

classification 💻 cs.DC
keywords energy efficient schedulingmulti-instance GPUsdynamic repartitioningreinforcement learningAI/ML workloadsjob schedulingdata center optimizationmulti-objective optimization
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The pith

Dynamic repartitioning of Multi-Instance GPUs via reinforcement learning cuts combined energy use and job tardiness by 26 to 68 percent versus static or infrequent alternatives.

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

Data centers are seeing sharp rises in power draw from AI and machine learning jobs. NVIDIA Multi-Instance GPUs let a single card be split into isolated compute slices, creating new scheduling choices. The paper builds a framework that first picks a job-to-slice assignment rule, then trains a reinforcement learning agent to resize the slices repeatedly across a day. Simulations that replay a repeating daily workload pattern taken from production traces show the learned policy beats fixed slices, twice-daily changes, and no slicing at all on a score that adds energy consumed to job lateness. The results also identify which slice sizes work best at different hours under varying queue lengths.

Core claim

The paper presents a dynamic repartitioning scheduling framework for a single MIG as a solution to a multi-objective heterogeneous machine scheduling problem with preemption. Four scheduling algorithms are compared and one is selected; reinforcement learning is then applied to choose MIG configurations over time. On a diurnal workload derived from real data center traces the learned policy improves the combined energy-tardiness objective by 26 percent over twice-daily repartitioning, 31 percent over static partitioning, and 68 percent over no partitioning, while also revealing time-of-day preferences for particular slice sizes.

What carries the argument

Reinforcement learning policy that selects both job assignments and MIG slice configurations at each time step to optimize the joint energy-plus-tardiness objective.

If this is right

  • The dynamic policy improves the multi-objective score by 31 percent relative to any fixed MIG slice size.
  • Twice-daily repartitioning is outperformed by 26 percent on the same combined metric.
  • Running without any repartitioning is outperformed by 68 percent.
  • Preferred slice sizes shift predictably with time of day and instantaneous queue length.
  • The learned preferences can be turned into a simple predictive rule for automatic daily reconfiguration.

Where Pith is reading between the lines

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

  • Operators could lower peak power draw in GPU clusters by applying the same time-of-day slice schedule without buying new hardware.
  • The same reinforcement learning loop could be tested on other sliceable accelerators once their performance and preemption models are available.
  • Adding a short-term workload forecast as extra input to the RL agent might further reduce the remaining tardiness penalty.
  • Production rollout would still need to verify that real preemption costs match the values used in the simulator.

Load-bearing premise

The repeating daily workload pattern taken from existing traces will continue to describe future production traffic and the simulator correctly reproduces MIG slice behavior and preemption overhead.

What would settle it

Running the learned dynamic policy on live MIG hardware with real AI/ML jobs for a full day and measuring actual energy draw and job completion delays against the same baselines used in simulation.

Figures

Figures reproduced from arXiv: 2606.25082 by Asser Tantawi, Clifford Stein, Connor Espenshade, Ellie Lipe, Neel Karia, Olivier Tardieu.

Figure 1
Figure 1. Figure 1: Slice Configurations of A100 40GB with MIG [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Linear, Capped and Sublinear Throughputs [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Power Characteristics of A100 at 250W Cap [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Preemption Frequency Across Configurations [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Variation of Arrival Rate Throughout 24-Hour Day [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: ET Average Percentage Time Spent Per Utilization Level [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: ET Across Configurations: Arrival Rate 0.1 would more quickly finish the longer-running training jobs and reduce the opportunity for overlap that EDF-SS capitalized off of. Additionally, this slowest slice allocation paired specifically with the earliest deadline job prioritization, as opposed to least laxity, ensures timely completion of shorter jobs; least laxity can prioritize longer-running jobs with l… view at source ↗
Figure 8
Figure 8. Figure 8: ET Across Configurations: Arrival Rate 0.75 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: ET Across Configurations: Inference Split 20% [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: ET Across Configurations: Inference Split 80% C. Dynamic Repartitioning Analysis We evaluate the results of the dynamic repartitioning model against three benchmarks: No MIG, Static MIG, and Day/Night Repartitioning MIG. These benchmarks are cho￾sen to provide realistic comparisons to existing scheduling approaches with MIG; while MIG currently can technically be repartitioned, implementing dynamic repart… view at source ↗
Figure 11
Figure 11. Figure 11: Overall, configurations 2, 3, and 6 are the most [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 11
Figure 11. Figure 11: Preferred Configurations by 4-Hour Interval [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
read the original abstract

Increasing demand from AI/ML workloads is exacerbating the rising energy consumption of data centers. Recent advances in hardware such as NVIDIA's Multi Instance GPUs (MIGs) offer improvements in flexibility and computational power and the opportunity for data centers to manage incoming jobs in energy-efficient ways, while maintaining acceptable performance. The challenge in achieving this multi-objective in a MIG environment through job scheduling is multi-faceted. Firstly, for a given MIG configuration, one seeks an easy-to-implement scheduling algorithm which selects a job from the queue as well as decides on which slice in the configuration the job runs. Secondly, for the identified scheduling algorithm, a particular MIG configuration may not always be suitable (as the workload fluctuates) and may need to be repartitioned. We tackle both problems using simulations and reinforcement learning (RL). We present a dynamic repartitioning scheduling framework for a single MIG as a solution to a multi-objective heterogeneous machine scheduling problem with preemption. In particular, we compare four scheduling algorithms and identify a promising one. Then, we employ reinforcement learning to perform dynamic repartitioning over a day. Furthermore, using a diurnal workload pattern based on real-world data center traces, we demonstrate the superiority of our dynamic repartitioning algorithm over twice-daily repartitioning ($26\%$), static partitioning ($31\%$) and no partitioning at all ($68\%$) according to a multi-objective function of energy consumption and tardiness. Our results indicate specific preferred configurations at different times of the day under different queue conditions, suggesting a policy for predictive and automatic reconfiguration.

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

4 major / 0 minor

Summary. The paper presents a dynamic repartitioning scheduling framework for Multi-Instance GPUs (MIGs) using reinforcement learning to address multi-objective optimization of energy consumption and job tardiness for AI/ML workloads. It evaluates four scheduling algorithms via simulation and then applies RL for dynamic MIG repartitioning over a day, claiming 26%, 31%, and 68% improvements over twice-daily repartitioning, static partitioning, and no partitioning, respectively, based on a multi-objective function using diurnal workload patterns from real-world data center traces.

Significance. If the simulation model were validated and the workload trace shown to be robust, the approach could offer a practical method for energy-aware scheduling on heterogeneous GPU hardware by identifying time-of-day preferred MIG configurations. The combination of discrete-event simulation with RL for repartitioning decisions is a reasonable direction for multi-objective heterogeneous machine scheduling.

major comments (4)
  1. [Abstract] Abstract: the multi-objective function of energy consumption and tardiness is never defined (no weights, normalization, or aggregation method), yet the central quantitative claims of 26%, 31%, and 68% superiority rest entirely on this undefined metric.
  2. [Abstract] Abstract: no error bars, number of simulation replications, or statistical significance tests accompany the reported percentage improvements, so the reliability of the superiority margins cannot be assessed.
  3. [Abstract] Abstract: the discrete-event simulator's models for MIG slice allocation, preemption latency, and power draw receive no hardware calibration or sensitivity analysis, yet the effect sizes (26-68%) are smaller than plausible modeling errors in these quantities.
  4. [Abstract] Abstract: the single diurnal arrival pattern extracted from data-center traces is used without cross-trace validation or ablation on job-size distribution or arrival-rate perturbations, undermining the claim that the learned policy's advantage is general.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address each major comment below and will revise the manuscript to strengthen the presentation of the multi-objective metric, statistical reporting, simulator robustness, and workload generality.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the multi-objective function of energy consumption and tardiness is never defined (no weights, normalization, or aggregation method), yet the central quantitative claims of 26%, 31%, and 68% superiority rest entirely on this undefined metric.

    Authors: We agree the abstract omits an explicit definition. We will revise the abstract to state that the multi-objective function is a weighted sum of min-max normalized energy consumption and tardiness (with equal weights of 0.5), aggregated as their arithmetic mean. This definition will be added directly to the abstract while preserving the reported improvements. revision: yes

  2. Referee: [Abstract] Abstract: no error bars, number of simulation replications, or statistical significance tests accompany the reported percentage improvements, so the reliability of the superiority margins cannot be assessed.

    Authors: We acknowledge the absence of these details in the abstract. The simulations underlying the 26/31/68% figures were performed with multiple replications; we will add error bars, state the replication count, and note that the reported differences are statistically significant in the revised abstract. revision: yes

  3. Referee: [Abstract] Abstract: the discrete-event simulator's models for MIG slice allocation, preemption latency, and power draw receive no hardware calibration or sensitivity analysis, yet the effect sizes (26-68%) are smaller than plausible modeling errors in these quantities.

    Authors: This point is valid. The models follow NVIDIA MIG specifications and published GPU power models, but no hardware calibration or sensitivity analysis appears in the current version. We will add a dedicated sensitivity analysis varying preemption latency and power-draw parameters by ±20% and demonstrate that the relative advantages of dynamic repartitioning remain stable. revision: yes

  4. Referee: [Abstract] Abstract: the single diurnal arrival pattern extracted from data-center traces is used without cross-trace validation or ablation on job-size distribution or arrival-rate perturbations, undermining the claim that the learned policy's advantage is general.

    Authors: We used one representative diurnal trace to isolate time-of-day effects. To strengthen generality claims we will incorporate an ablation study perturbing arrival rates and job-size distributions, plus results on at least one additional trace from the same data-center dataset, confirming that the RL policy retains its advantage. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain; evaluation is simulation-driven

full rationale

The paper reports empirical simulation results comparing an RL dynamic-repartitioning policy against fixed baselines on a multi-objective metric, using a diurnal arrival pattern extracted from traces. No equations, derivations, or self-citations appear in the provided text that would reduce any claimed result to its own inputs by construction. The performance margins (26/31/68 %) are outputs of the discrete-event simulator rather than tautological re-statements of fitted parameters or imported uniqueness theorems. The central claims therefore rest on modeling assumptions about MIG behavior and trace representativeness, which fall outside the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be identified from the provided text.

pith-pipeline@v0.9.1-grok · 5833 in / 1052 out tokens · 28319 ms · 2026-06-25T22:31:58.909015+00:00 · methodology

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