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Paper Citation Record · LEDGER

Topology-aware Preemptive Scheduling for Co-located LLM Workloads

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2411.11560.

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

pith.paper-citation-record.v1
2411.11560 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:28:01.641782Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

42 of 42 outbound references displayed

  • verified exact0
  • verified fuzzy31
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f64c548-f65d-4eba-b4dd-645c130c0fbc · outbound

This paper cites A survey on large language models: Applications, challenges, limitations, and practical usage.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads A survey on large language models: Applications, challenges, limitations, and practical usage

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:28:01.428717Z digest=sha256:10e42335b335cda6683f806c0a78b9ea2d58af45c20f09ff6eeb2562569acb4c

Observation b3303931-8436-4e1c-b87e-27fcafcf2e7a · outbound

This paper cites Efficient Training of Large Language Models on Distributed Infrastructures: A Survey.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Efficient Training of Large Language Models on Distributed Infrastructures: A Survey

Reference 2

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source=pdf_text observed=2026-08-12T18:28:01.433698Z digest=sha256:9937ab8feeb0a2d135dc4f34035ea5f8566a3aece0f65e80c872bf92e33ac381

Observation decb8d3c-e512-4ab7-a628-a57eb2b26359 · outbound

This paper cites BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems

Reference 3

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source=pdf_text observed=2026-08-12T18:28:01.438721Z digest=sha256:f4dbb014995d0a86e1b8965f639bd5966dd576ad06863895a51bbabce092656b

Observation 1838389c-2088-4752-ae94-a643ce2ca9f1 · outbound

This paper cites Gödel: Unified large-scale resource management and scheduling at bytedance.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Gödel: Unified large-scale resource management and scheduling at bytedance

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.443368Z digest=sha256:ddbb4e4c51b22748d758985d97634683fcd56fa528f288f78e2be27afedae40a

Observation 686d5a79-898d-46f3-9724-e2a7a3495913 · outbound

This paper cites Topology-aware gpu scheduling for learning workloads in cloud environments.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Topology-aware gpu scheduling for learning workloads in cloud environments

Reference 5

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation cb7af90d-5efa-4522-b89b-27aadcf7a300 · outbound

This paper cites Numa (non-uniform memory access): An overview: Numa becomes more common because memory controllers get close to execution units on microprocessors.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Numa (non-uniform memory access): An overview: Numa becomes more common because memory controllers get close to execution units on microprocessors

Reference 6

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.452715Z digest=sha256:84602fa581c0d1878b1de8160e010b42c620254728d13dfd8d8883ecf69c10f9

Observation c2a87714-bfe2-4b13-bb56-e6dce973d287 · outbound

This paper cites M\'elange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads M\'elange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity

Reference 7

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source=pdf_text observed=2026-08-12T18:28:01.457638Z digest=sha256:433964cd7b7005a608a2b39c664103e971298074bd680e94a49e522cc4e123ed

Observation b274c18f-8aa3-4049-8c3b-ef85184042f6 · outbound

This paper cites Fastertransformer.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Fastertransformer

Reference 8

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.462536Z digest=sha256:fde2fe61e47a1360372c5d3f13a8281aa805a02ed9ea34a35c1049e2392cd536

Observation d72f1f46-7198-4ed8-b7e6-929dac152349 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Efficient memory management for large language model serving with pagedattention

Reference 9

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source=pdf_text observed=2026-08-12T18:28:01.466924Z digest=sha256:142417abcb3f5490057ba7c269c95139d0a2b7d2716c207105fd56df428934dd

Observation 8059a009-b02e-4a5e-90ab-5b5462e55ffc · outbound

This paper cites an unresolved cited work.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Unresolved cited work

Reference 10

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.471222Z digest=sha256:d4b4f500c89a2cfbcd4477af5a2761edb438d1fbf444af6077ef0739c204657b

Observation 55bbb4f4-33c7-4400-9a72-8dedbf17fc3d · outbound

This paper cites Huggingface text generation inference.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Huggingface text generation inference

Reference 11

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.476060Z digest=sha256:f3bd456f6d2407b6f49275e3bdf262da9bd3b38ac0547d82d7b445bcc2806d61

Observation 8cf4ed1a-5c32-4f90-8fc4-6828229232e4 · outbound

This paper cites Deepspeed inference.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Deepspeed inference

Reference 12

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.481104Z digest=sha256:440b472e02c91cde9f960dc69a996494e14399ac8c9e079e7013425e3a4ad394

Observation 24929857-98da-480d-bb69-5d8d84af3978 · outbound

This paper cites Tensorrt-llm.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Tensorrt-llm

Reference 13

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.486113Z digest=sha256:dad8365d3ce9a7221af6387b4c9fcf43c9ab06403e2a5e29721f89fbdfff1f62

Observation c33301c8-4278-4565-8476-81bae1b4adad · outbound

This paper cites Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems

Reference 14

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source=pdf_text observed=2026-08-12T18:28:01.491752Z digest=sha256:b9ef0e0214f86bf7406a3964e1fbb011f9809103185ce4aabebc668d1499b210

Observation 35e1fa7c-29a6-48ae-8bf0-d7501de9fdf6 · outbound

This paper cites Kubernetes topology manager moves to beta.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Kubernetes topology manager moves to beta

Reference 15

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.497305Z digest=sha256:b7c98f5e4f12c95d61faa5bb954f86a246eeac0fb700a4519044cffb60683c08

Observation 4a4273a1-70b4-4d0e-a9fb-d136a6eafbe2 · outbound

This paper cites Pod priority and preemption.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Pod priority and preemption

Reference 16

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.502510Z digest=sha256:1c880efbb8a656ccace053e75954488daaa88b8d206cb3df2d973bf1d4827664

Observation 56641ff5-55ad-4d69-a0ed-47f142ee90b4 · outbound

This paper cites Godel scheduler: a unified scheduler for online and offline tasks.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Godel scheduler: a unified scheduler for online and offline tasks

Reference 17

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.507707Z digest=sha256:399fd99591112a6f94f11735d62595419bf084ae8ddcbef284795c6895de35eb

Observation 65743995-cd8e-4356-bc95-b0d38e2e1028 · outbound

This paper cites Daemonset.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Daemonset

Reference 18

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.513084Z digest=sha256:884912436f038a29326fab541803b73e42e5836e244f0e13b6247b00daa39f19

Observation 38571f2a-df86-4258-95d3-9677b53151bd · outbound

This paper cites Kubernetes without kubelet.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Kubernetes without kubelet

Reference 19

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.518354Z digest=sha256:54faf8e13c6fee813a2786c9727bb6a96b8db5c53e13c5139c0d9698312535a2

Observation 60eb0db1-4c97-4a32-ab61-2449af02ad9f · outbound

This paper cites Control topology management policies on a node.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Control topology management policies on a node

Reference 20

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.523308Z digest=sha256:7984b3d45ca043c3ed0539b116434053944b517ce80b4df1f79b9d29817afcbe

Observation 88a88c86-aed8-4e04-8cf0-847c994707ee · outbound

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

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Towards {GPU} utilization prediction for cloud deep learning

Reference 21

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source=pdf_text observed=2026-08-12T18:28:01.528634Z digest=sha256:e8fb5365b358fe6fe1f4926794dc861c588665e5d3b07c831e90aac232dd1b69

Observation b1a8bceb-c2a4-40f6-8530-0d45e9a5ee3f · outbound

This paper cites Horus: Interference-aware and prediction-based scheduling in deep learning systems.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Horus: Interference-aware and prediction-based scheduling in deep learning systems

Reference 22

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source=pdf_text observed=2026-08-12T18:28:01.533781Z digest=sha256:0c875a0524ec977f9b991545aa814aecc0877a23d1aa7d4cb828a013b01cf9aa

Observation 925f7790-266f-4f84-b56e-a683690cf4c0 · outbound

This paper cites Beware of fragmentation: Scheduling {GPU-Sharing} workloads with fragmentation gradient descent.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Beware of fragmentation: Scheduling {GPU-Sharing} workloads with fragmentation gradient descent

Reference 23

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source=pdf_text observed=2026-08-12T18:28:01.539007Z digest=sha256:3df0cd61ad6f4d3a61ac0ac1215cf103f89bba7dd71280c76f0429335c2b1fad

Observation b9e132b9-40af-44c1-bf05-587c08e6e83b · outbound

This paper cites {HiveD}: Sharing a {GPU} cluster for deep learning with guarantees.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads {HiveD}: Sharing a {GPU} cluster for deep learning with guarantees

Reference 24

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source=pdf_text observed=2026-08-12T18:28:01.543941Z digest=sha256:d7ca1205604f9f36247638aaf68bdd12573a98b43afc9fb9730ec9cc76207a30

Observation a71a4c0e-220e-4f0c-b4f4-ff8a56479a6f · outbound

This paper cites Supporting gpu sharing in cloud environments with a transparent runtime consolidation framework.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Supporting gpu sharing in cloud environments with a transparent runtime consolidation framework

Reference 25

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.549509Z digest=sha256:f612c4ef8f0e8a88cd5fd1af6ab0e8e7892bb9eab2e0d9105162de2d8092f19f

Observation 748ddaea-6be4-49c5-ac6c-c15e16774fd0 · outbound

This paper cites Fine-grained gpu sharing primitives for deep learning applications.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Fine-grained gpu sharing primitives for deep learning applications

Reference 26

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.554580Z digest=sha256:b31daac7b9cfd93319f3cdfdde3a9636ae95225c0a8b2620ae37c1276e891609

Observation 8223a453-87fd-43c1-93e8-110117107960 · outbound

This paper cites Gpushare: Fair-sharing middleware for gpu clouds.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Gpushare: Fair-sharing middleware for gpu clouds

Reference 27

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.560039Z digest=sha256:f6f62766c87a6dc9e7d52753e0972aba8c1908ec79e533f4781a17e86305b3c2

Observation 4506a771-c34a-4ba0-9258-99d951452b22 · outbound

This paper cites Nvidia cloud native technologies: Gpu sharing.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Nvidia cloud native technologies: Gpu sharing

Reference 28

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.565070Z digest=sha256:ddbfd895e5319004c36ec63abecb63113dfd13f902f2fa15b7dcba5b7ddbc359

Observation 4c09c855-110c-49b5-b9a1-3475a4e1f3c7 · outbound

This paper cites Advanced features in ibm power8 systems.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Advanced features in ibm power8 systems

Reference 29

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source=pdf_text observed=2026-08-12T18:28:01.570281Z digest=sha256:0775bf447bf86cb02df9b2788af785e1abaad5366e3829995204763e312f0a87

Observation 1fb6f2b0-c3d3-45af-a511-191befec4d7f · outbound

This paper cites Performance evaluation of the nvidia tesla p100: Our directive-based partitioning and pipelining vs.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Performance evaluation of the nvidia tesla p100: Our directive-based partitioning and pipelining vs

Reference 30

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.575734Z digest=sha256:1b8d5cde8e3f1fed6ea3d5c882175d32bacbec0b153eaff64d6fecf723de5738

Observation deeb195f-5236-4900-8171-18ad8d71598f · outbound

This paper cites Topology-aware scheduling framework for microservice applications in cloud.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Topology-aware scheduling framework for microservice applications in cloud

Reference 31

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.580861Z digest=sha256:2ae247fc6bc87e1bd913b1fb1d306e75b0293f6bd6846ed965b8318821dd8612

Observation dbf852d4-d6ba-44a7-a2ef-b31305b0d091 · outbound

This paper cites Katalyst core.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Katalyst core

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.586803Z digest=sha256:9db62fa3017997a8315bf8c2ee204e26de248f3df6f698e35602e22ff395bcf3

Observation 93263d25-13c5-437c-9a69-5016325fcf88 · outbound

This paper cites Topology-aware resource allocation for data-intensive workloads.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Topology-aware resource allocation for data-intensive workloads

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.592516Z digest=sha256:933b71ab11e83ef5dc34834e91fb9bf2f71df809ce95ff60308851a883960e11

Observation 6f4c2c72-2123-4979-afa7-c94df6b035d9 · outbound

This paper cites Towards topology aware pre-emptive job scheduling with deep reinforcement learning.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Towards topology aware pre-emptive job scheduling with deep reinforcement learning

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.597592Z digest=sha256:c55602d2df3bdfb525ff33d9205399ca8a4d1e7096ee7c840fa39ab15b4c1a12

Observation 792f1e0a-0f86-4e97-a7ec-91a733a6150d · outbound

This paper cites Microsecond-scale preemption for concurrent {GPU- accelerated}{DNN} inferences.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Microsecond-scale preemption for concurrent {GPU- accelerated}{DNN} inferences

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-12T18:28:01.849163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.603019Z digest=sha256:24060c3110af9ab4bfad3b1ec4e4ed9f7b6e12a2fed1e8240540027400ab2533

Observation cc0d2b9d-a5d2-457a-9492-ffaf42566c25 · outbound

This paper cites Efficiently programming large language models using sglang.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Efficiently programming large language models using sglang

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:28:01.830860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.608437Z digest=sha256:8642084e0a31ea81f0c3d47adb097762033ad236c529182efff4d8c005de8988

Observation d548ec38-5922-4dd3-826e-3af1d996ebbd · outbound

This paper cites Orca: A distributed serving system for {Transformer-Based} generative models.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Orca: A distributed serving system for {Transformer-Based} generative models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-12T18:28:01.614459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:28:01.614459Z digest=sha256:86b9137bfd403f87f170e7fea85ec37463d5676d1ecea265bed76df49d1b6aef

Observation 9ff1a8a3-098a-48de-9bd6-1c601afba6c4 · outbound

This paper cites Fast Distributed Inference Serving for Large Language Models.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Fast Distributed Inference Serving for Large Language Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-12T18:28:01.620434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:28:01.620434Z digest=sha256:ca77399e7e347ef793c832e12c9b2758164c88fcf0accbf77dfd85e9685164d3

Observation 799a9f9b-e866-443b-a8f8-1e266cf3c4c9 · outbound

This paper cites Bert loses patience: Fast and robust inference with early exit.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Bert loses patience: Fast and robust inference with early exit

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:28:01.801769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.625813Z digest=sha256:ce4ae31a4f1fa542aa94b7d0e80adf9067b145062b25da4569d9441da1e9bb34

Observation 2ef5b5fb-8604-47f4-9f99-e25ed9042c7d · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T18:28:01.631009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:28:01.631009Z digest=sha256:0a89b89db447aacfb589f9361f498ce8c90a7c52303033ba398c2db54f1df8e9

Observation a7a7f4e6-21d2-45c6-bc1d-78087adeda08 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T18:28:01.636569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:28:01.636569Z digest=sha256:bce880beb52116193c466c47a8d983e4cd1489f44269e04d71c364b71b2ad8b9

Observation 563b8ecf-5b3a-47ba-af67-92051c370136 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

Topology-aware Preemptive Scheduling for Co-located LLM Workloads Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:28:01.763314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-12T18:28:01.641782Z digest=sha256:27fb76652b7d6ff45ae3a0f4ff2456cf629a4da77162593191ab29e3215be8ee

Pith citing papers

No inbound Pith citation observations are available.