Paper Citation Record · LEDGER

Managing Multi Instance GPUs for High Throughput and Energy Savings

As of 20 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2508.18556.

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

pith.paper-citation-record.v1
2508.18556 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:01:11.474559Z

measured 33 of 33 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

33 of 33 outbound references displayed

verified exact0
verified fuzzy27
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c3504fab-fa52-4d26-91fb-9641fbdc3e10 · outbound

This paper cites Basaran and K.

Managing Multi Instance GPUs for High Throughput and Energy Savings Basaran and K

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.747815Z

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 7b444fa6-3a4e-4640-ab06-c09995a47573 · outbound

This paper cites Sheaffer, Sang-Ha Lee, and Kevin Skadron.

Managing Multi Instance GPUs for High Throughput and Energy Savings Sheaffer, Sang-Ha Lee, and Kevin Skadron

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.740990Z

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 4f5d1196-06ff-47f7-a15a-53d946d4c262 · outbound

This paper cites Sheaffer, Michael Boyer, Lukasz G.

Managing Multi Instance GPUs for High Throughput and Energy Savings Sheaffer, Michael Boyer, Lukasz G

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.733648Z

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 b70b65e9-eeb0-4891-91cc-a4c1eec577a7 · outbound

This paper cites CASE: a compiler-assisted scheduling framework for multi-gpu systems.

Managing Multi Instance GPUs for High Throughput and Energy Savings CASE: a compiler-assisted scheduling framework for multi-gpu systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.726532Z

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 fa049099-a881-45da-873f-ee04d0849c73 · outbound

This paper cites Zhao, Yanping Huang, Andrew M.

Managing Multi Instance GPUs for High Throughput and Energy Savings Zhao, Yanping Huang, Andrew M

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.718855Z

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 9c24ba7e-4940-476d-a461-00dbaa939d5c · outbound

This paper cites The Llama 3 Herd of Models.

Managing Multi Instance GPUs for High Throughput and Energy Savings The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T17:01:11.401482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d3e4dba-1d96-4da2-8ec4-68795f311e39 · outbound

This paper cites Estimating GPU memory consumption of deep learning models.

Managing Multi Instance GPUs for High Throughput and Energy Savings Estimating GPU memory consumption of deep learning models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.711659Z

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 04a47645-7c46-4e18-93a1-c7699ee41769 · outbound

This paper cites Characterization and prediction of deep learning workloads in large-scale GPU datacenters.

Managing Multi Instance GPUs for High Throughput and Energy Savings Characterization and prediction of deep learning workloads in large-scale GPU datacenters

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.704515Z

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 838427d3-99be-4601-af1e-cc51e38380bb · outbound

This paper cites Gdev: First-class GPU resource manage- ment in the operating system.

Managing Multi Instance GPUs for High Throughput and Energy Savings Gdev: First-class GPU resource manage- ment in the operating system

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.696431Z

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 fcad5c01-10e7-4e59-991d-5c8f801599f7 · outbound

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

Managing Multi Instance GPUs for High Throughput and Energy Savings MISO: exploiting multi- instance GPU capability on multi-tenant GPU clusters

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.688905Z

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 1e0ff1d3-55de-4abf-9d8b-4f078c530b7c · outbound

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

Managing Multi Instance GPUs for High Throughput and Energy Savings Clover: Toward sustainable AI with carbon- aware machine learning inference service

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.681492Z

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 c5734c8f-9169-4a2a-bfd6-4691cd139384 · outbound

This paper cites Zico: Efficient GPU memory sharing for concurrent DNN training.

Managing Multi Instance GPUs for High Throughput and Energy Savings Zico: Efficient GPU memory sharing for concurrent DNN training

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.673650Z

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 aaaa46e7-99d0-4558-a90f-42d3fe231249 · outbound

This paper cites an unresolved cited work.

Managing Multi Instance GPUs for High Throughput and Energy Savings Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:01:11.665719Z

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 0e4fe565-1f4c-4e5b-a145-52c580896b4d · outbound

This paper cites Mig user guide.

Managing Multi Instance GPUs for High Throughput and Energy Savings Mig user guide

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.657981Z

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 230d312f-f51a-469c-83c0-b0cb9f7799d3 · outbound

This paper cites Chimera: Collaborative preemption for multitasking on a shared gpu.

Managing Multi Instance GPUs for High Throughput and Energy Savings Chimera: Collaborative preemption for multitasking on a shared gpu

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.649151Z

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 4c580ef9-0f82-4ff8-82f4-19c58bf9f5cb · outbound

This paper cites Compiler- assisted scheduling for multi-instance gpus.

Managing Multi Instance GPUs for High Throughput and Energy Savings Compiler- assisted scheduling for multi-instance gpus

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.641551Z

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 2930dbc5-eed7-4ab1-9de1-f6e6f1b2c4e1 · outbound

This paper cites Rossbach, Jon Currey, Mark Silberstein, Baishakhi Ray, and Emmett Witchel.

Managing Multi Instance GPUs for High Throughput and Energy Savings Rossbach, Jon Currey, Mark Silberstein, Baishakhi Ray, and Emmett Witchel

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.633924Z

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 bb2074df-48b4-458a-9c39-5be6bb05404b · outbound

This paper cites A preemption-based runtime to efficiently schedule multi-process applications on heterogeneous clusters with gpus.

Managing Multi Instance GPUs for High Throughput and Energy Savings A preemption-based runtime to efficiently schedule multi-process applications on heterogeneous clusters with gpus

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.626248Z

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 2b587bce-d3fe-4f4e-8c31-71f7413e24bb · outbound

This paper cites Samuel, Stephen McNally, and John Wynkoop.

Managing Multi Instance GPUs for High Throughput and Energy Savings Samuel, Stephen McNally, and John Wynkoop

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.618633Z

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 8e1d0dbd-b1e2-40db-951c-5a3564d66fe6 · outbound

This paper cites Junkyard computing: Repurposing dis- carded smartphones to minimize carbon.

Managing Multi Instance GPUs for High Throughput and Energy Savings Junkyard computing: Repurposing dis- carded smartphones to minimize carbon

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.611185Z

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 988b1264-4700-4582-a371-e6fdcb4a0a28 · outbound

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

Managing Multi Instance GPUs for High Throughput and Energy Savings Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T17:01:11.442960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78681217-0c22-4bd1-98dc-945455b6f149 · outbound

This paper cites Gpupool: A holistic approach to fine-grained GPU sharing in the cloud.

Managing Multi Instance GPUs for High Throughput and Energy Savings Gpupool: A holistic approach to fine-grained GPU sharing in the cloud

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.603549Z

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 eef68c99-3a5c-4c75-927b-3bf30146556f · outbound

This paper cites Tanasic, I.

Managing Multi Instance GPUs for High Throughput and Energy Savings Tanasic, I

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.595808Z

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 8810e470-1728-49c5-9833-2c00fcf83b66 · outbound

This paper cites Pcie bandwidth-aware scheduling for multi-instance gpus.

Managing Multi Instance GPUs for High Throughput and Energy Savings Pcie bandwidth-aware scheduling for multi-instance gpus

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.587655Z

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 6a933c57-1ad9-486b-8ba4-9d3a08725033 · outbound

This paper cites Improving GPU Multi-Tenancy Through Dynamic Multi-Instance GPU Reconfiguration.

Managing Multi Instance GPUs for High Throughput and Energy Savings Improving GPU Multi-Tenancy Through Dynamic Multi-Instance GPU Reconfiguration

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T17:01:11.452853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c5be05e-e153-430f-8dfc-5f6e16899a11 · outbound

This paper cites Mlaas in the wild: Workload analysis and scheduling in large-scale heterogeneous GPU clus- ters.

Managing Multi Instance GPUs for High Throughput and Energy Savings Mlaas in the wild: Workload analysis and scheduling in large-scale heterogeneous GPU clus- ters

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.578117Z

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 d7f24c28-0164-4abb-833d-0bdfbf12f9db · outbound

This paper cites Flep: Enabling flexible and efficient preemption on gpus.

Managing Multi Instance GPUs for High Throughput and Energy Savings Flep: Enabling flexible and efficient preemption on gpus

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.568082Z

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 920c56ae-7dfa-49bf-b8c8-4ca65fd792c0 · outbound

This paper cites Gandiva: Introspective cluster scheduling for deep learning.

Managing Multi Instance GPUs for High Throughput and Energy Savings Gandiva: Introspective cluster scheduling for deep learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.558694Z

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 0fb78145-9af8-4e7e-bc06-a92d4ad18b5e · outbound

This paper cites Qwen2 Technical Report.

Managing Multi Instance GPUs for High Throughput and Energy Savings Qwen2 Technical Report

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T17:01:11.463179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bf8331c-bc8c-445f-b076-12adf9dfa043 · outbound

This paper cites Towards GPU utilization prediction for cloud deep learning.

Managing Multi Instance GPUs for High Throughput and Energy Savings Towards GPU utilization prediction for cloud deep learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.548984Z

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 1c407144-c540-41c3-a67f-09577b4767b2 · outbound

This paper cites Young, Jason Riedy, Thomas M.

Managing Multi Instance GPUs for High Throughput and Energy Savings Young, Jason Riedy, Thomas M

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.540218Z

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 d2826218-ee10-4007-ae4d-66c383927ff1 · outbound

This paper cites an unresolved cited work.

Managing Multi Instance GPUs for High Throughput and Energy Savings Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:01:11.532023Z

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 248bc2a2-6615-4430-b5a8-a4d945f4713b · outbound

This paper cites Table 2: The ML mixes used in the experiments.

Managing Multi Instance GPUs for High Throughput and Energy Savings Table 2: The ML mixes used in the experiments

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:01:11.523993Z

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.

Pith citing papers

No inbound Pith citation observations are available.