Paper Citation Record · LEDGER

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

As of 19 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2606.25082.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.25082 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-25T22:34:03.839748Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact1
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 425c4931-d358-4daf-9c88-ec88f3d837b2 · outbound

This paper cites AI is pushing the world toward an energy crisis. forbes.,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning AI is pushing the world toward an energy crisis. forbes.,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a5264fc-8579-4dcf-b144-baa044c05d24 · outbound

This paper cites Performance-aware energy-efficient GPU frequency selection using DNN-based models,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Performance-aware energy-efficient GPU frequency selection using DNN-based models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cf75fab-cd1c-41bc-baa5-b1168a8b2fca · outbound

This paper cites Managing queues with heteroge- neous servers,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Managing queues with heteroge- neous servers,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

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Unavailable: canonical work link unavailable.

Observation b3b1cda1-3a01-46e3-8abc-95835516cae5 · outbound

This paper cites Approximations in performance analysis of a controllable queueing system with heteroge- neous servers,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Approximations in performance analysis of a controllable queueing system with heteroge- neous servers,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a971516-3ffa-404d-980c-33073b5fa024 · outbound

This paper cites Complexity of preemptive minsum scheduling on unrelated parallel machines,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Complexity of preemptive minsum scheduling on unrelated parallel machines,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6da3bef-f657-4609-9cad-ba79f4dea243 · outbound

This paper cites Paris and elsa: an elastic scheduling al- gorithm for reconfigurable multi-gpu inference servers,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Paris and elsa: an elastic scheduling al- gorithm for reconfigurable multi-gpu inference servers,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72e1ecbe-4a5c-49f8-8d17-b54edacba511 · outbound

This paper cites Serv- ing dnn models with multi-instance gpus: A case of the reconfigurable machine scheduling problem,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Serv- ing dnn models with multi-instance gpus: A case of the reconfigurable machine scheduling problem,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f3e91e4-a20d-4846-9260-84a605d523e8 · outbound

This paper cites Clover: Toward sus- tainable AI with carbon-aware machine learning inference service,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Clover: Toward sus- tainable AI with carbon-aware machine learning inference service,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 179d9c82-5274-447d-a87e-2837731f3fd5 · outbound

This paper cites Great power, great responsibility: Recommendations for reducing en- ergy for training language models,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Great power, great responsibility: Recommendations for reducing en- ergy for training language models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67f416c1-17c6-4024-a6b7-569c4269ab0c · outbound

This paper cites Zeus: Understanding and optimizing GPU energy consumption of DNN training,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Zeus: Understanding and optimizing GPU energy consumption of DNN training,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20d9d4c0-b508-4400-a14d-6c07dc4d78d4 · outbound

This paper cites Reinforcement learning applications to machine scheduling problems: a comprehensive literature review,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Reinforcement learning applications to machine scheduling problems: a comprehensive literature review,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4194975-36a6-4fc3-9ceb-2bbea6efa8bf · outbound

This paper cites Resource man- agement with deep reinforcement learning,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Resource man- agement with deep reinforcement learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf85e4b1-cc25-4be6-b31e-ef5c672133b5 · outbound

This paper cites Hierarchical resource partitioning on modern GPUs: A reinforcement learning approach,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Hierarchical resource partitioning on modern GPUs: A reinforcement learning approach,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cb628a1-b9f7-4da8-9973-c99e6fe5b5bb · outbound

This paper cites A deep reinforcement learning-based task scheduling algorithm for energy efficiency in data centers,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning A deep reinforcement learning-based task scheduling algorithm for energy efficiency in data centers,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab9296b-1d15-4426-bc7e-d354e9659ff2 · outbound

This paper cites Toward efficient compute-intensive job allocation for green data centers: A deep reinforcement learning approach,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Toward efficient compute-intensive job allocation for green data centers: A deep reinforcement learning approach,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b43eaf97-3830-48b1-8fa1-2df8696553d7 · outbound

This paper cites Characterizing training performance and energy for foundation models and image classifiers on multi-instance GPUs,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Characterizing training performance and energy for foundation models and image classifiers on multi-instance GPUs,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e4d8fa6-4d3f-4143-9ba7-da94d116a474 · outbound

This paper cites MISO: Exploiting multi-instance GPU capability on multi-tenant GPU clusters,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning MISO: Exploiting multi-instance GPU capability on multi-tenant GPU clusters,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 851e5238-d8ed-4a1f-9a18-481fe9f5ec10 · outbound

This paper cites Characterizing multi- instance GPU for machine learning workloads,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Characterizing multi- instance GPU for machine learning workloads,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cae2280d-cf63-4988-8bf9-d787993ac4a3 · outbound

This paper cites Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-04T18:40:03.588241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a86d16a5-6c6c-4e86-8fcb-acbe21b623bf · outbound

This paper cites Parallel-machine scheduling to mini- mize tardiness penalty and power cost,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Parallel-machine scheduling to mini- mize tardiness penalty and power cost,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a857f70-2cd8-40bd-804d-f5aa4b4f8e92 · outbound

This paper cites Deep reinforcement learning: An overview,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning Deep reinforcement learning: An overview,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b47532ce-394d-4645-b3d1-abd03962e5fb · outbound

This paper cites MLaaS in the wild: Workload analysis and scheduling in Large-Scale heterogeneous GPU clusters,.

Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning MLaaS in the wild: Workload analysis and scheduling in Large-Scale heterogeneous GPU clusters,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-25T22:34:03.839748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.