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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=sha256:52bba20ca1b98621bf44801ee2cd01bc0ca7b669b244dece365dfb7864f2f6f9

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:ced77408abf88e540b59a60cc8384b16ebd88060023f1bee617141d4b7741720

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=sha256:65b3477c1bad603c6d98c4f7f10bfc8ce25200c2c54759fc3cb22aedd74a3888

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=sha256:2d1dc5a2e760c648f711615a525482d779ca526e771e83253dd69cab11c5f444

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.

source=pdf_text observed=2026-08-12T18:28:01.447617Z digest=sha256:1bc2e41bdbd1aca1fd9331f8a4f12831b413fe6933341ccb937eff5b05035f43

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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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=2026-08-12T18:28:01.452715Z digest=sha256:5f5c09c144742f919720f92eff2949c2922ff7458e4038097b97db568cd255e6

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=sha256:5bdfa6fdf286fdf0916e0f1c04aea461c7340e9cd975cda474c661e8d9a6b155

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:c05fa51670998db0e1fbef8192900438337e75423b4fd7f6605a0292ddcb0500

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=sha256:42e1c18b99f96ea97557e76172161a312c27143bbc292c611ea5e9aec2118ff0

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=sha256:835cf2915095f93d48d502a1426f7b035887cc56b4b6e887a0b46d1c661950fc

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=sha256:7208fbe509b29d31b2d94ed0377d4a6a4ca51684b1aa4d10ba9b612913ed61b6

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=sha256:790891aba65aa43844c4a54d15714ccef42ab9df5a65d4410bfb8acfacfd71e2

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=sha256:93ce6d6835c8a1a23961701952ac0cd5dc2546c55be40e7fda27c3b76c0ca716

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:e6d9af5e09c8517ca4433c63565bee33a17b46379708ad83406efce9087e1b38

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=sha256:5a4ed9943d9228c5f6045315918b121e44558c9a684ff1c4260518a917a7fee0

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=sha256:50c288cb905b2f6322f3d24f2edb8d0dc49ecad399361b5b80f6bfcef78be380

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=sha256:01d452f1d8b8a2c916ab4172eb9a9ea86930266060d5f689890b5717ab60176f

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=sha256:1592574ce3bbcc09dd38766a0237de1102df92384e8d7ff19738b229d8688ecd

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=sha256:2a43ac6992f62c5f3edcecd10b60183ff84ee1908c82ac255bb89b1eb6dd1a61

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:af0452e65e076a34f9e44299c1423c78a4a39880763ee17efc445b11940528da

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

source=pdf_text observed=2026-08-12T18:28:01.528634Z digest=sha256:86b38f5a6b94e26137f8e671c075e3bee918849d3edb06799967a8d045a353ae

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

source=pdf_text observed=2026-08-12T18:28:01.533781Z digest=sha256:b6bdb7ddad61ac539713878a9b8f02cd9984a022a0aa24b045d5a1ba5a670ee9

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

source=pdf_text observed=2026-08-12T18:28:01.539007Z digest=sha256:f01f1e98d2d6356d0514bfd4e2a494ff4845f45d4294f9a35617fd76cd854fb0

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=sha256:4b3d418e599e86a21595b41bcd0236117c9d56accc0d33adb41c3a067b78ec4a

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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source=pdf_text observed=2026-08-12T18:28:01.549509Z digest=sha256:15988f2f99675061ff7e6fe8899a27d732424052e879e2bfeed176b744df1a8b

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=sha256:3f886b62161a86b9c6b3179c2ebbdb3fce92ecb24b3dac31e44fc955cef72dba

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=sha256:5a5e4cd24d83b6d80f61d1d387137b9f50f40601475d30544f1f880a3fb1e642

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=sha256:46410b0d7477db5245a4310e0b8993b6830e816c0449948ff1bbaf0a5b66a62a

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:ef627615abd429441ffcf98bd1536e909321933775976289a478b3a56a96e3d4

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:b4184a0abbe3bac47b395145b132f6c801503cd76874aa6f3a7457287c8ba3c2

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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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=2026-08-12T18:28:01.580861Z digest=sha256:e149324bc746686b3117c6cd5c80aea5e05f25e84b87931b658ae458ebf40e9c

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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raw_fallback, observed 2026-08-12T18:28:01.901739Z

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=sha256:2b735e41617db6d8b8244fae25f2832c02fd7ba636366da1a155289c0de82d84

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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raw_fallback, observed 2026-08-12T18:28:01.884085Z

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=sha256:6bb05f612b5b065acdd5a309d51882df9e5cd21174d6b0c43d3929456f8dd192

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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raw_fallback, observed 2026-08-12T18:28:01.865992Z

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=sha256:376c79e55fc8c945b74cc3050899d8a24596cd481ae901a65bf937bc2aaac0f0

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:d30c452373863fb482e8a756329851791bd715ed64369936c3f743c5430e53e8

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=sha256:3c96c1f08c56396509b2b7642cf9d708c73b6cabbb2fcd11b082cfd675da53ac

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=sha256:371ddf360e40d7950fcd44b537632ee9c391bf3b7f669cddb4236b2712a42413

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:f5b59ee17810d4ebd7ca645fc081805e68d4db5c8ec786ffd7e758b3b3392576

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=sha256:40754dcb242c3b4e91d9b5f5b6f4a1d17b5d17bf8a0db77185b04bbb001332a5

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:a903241225e5976a7e3c447ff7e43165f8c5c5160c20e079aa21aa2243741605

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=sha256:54456e944112fca920da6544fb654d935c88ab384746c5ebbe1932cdf866b28d

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:ba244b9aa0da9d4a8603459f694e810c091ff4c20f8900cbf8968a5685eb7583

Pith citing papers

No inbound Pith citation observations are available.