{"as_of":"2026-08-17T21:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:635458cc29e1769610f526308e73590b9ad61e2ad0bd4e497623e033e5856e70","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T18:28:01.641782Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.11560/citation-record","integrity":"/paper/2411.11560/integrity","json":"/paper/2411.11560/citation-record.json","paper":"/paper/2411.11560"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.428717Z","title":"A survey on large language models: Applications, challenges, limitations, and practical usage","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.428717Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:10e42335b335cda6683f806c0a78b9ea2d58af45c20f09ff6eeb2562569acb4c","observation_id":"8f64c548-f65d-4eba-b4dd-645c130c0fbc","resolution":{"observed_at":"2026-08-12T18:28:01.428717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.20018","last_updated":"2024-07-29T13:53:27Z","snapshot_observed_at":"2026-08-16T13:30:12.966672Z","submitted_at":"2024-07-29T13:53:27Z","title":"Efficient Training of Large Language Models on Distributed Infrastructures: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.20018","snapshot_observed_at":"2026-08-12T18:28:01.433698Z","title":"Efficient training of large language models on distributed infrastructures: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.433698Z"},"links":{"cited_paper":"/paper/2407.20018","citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:9937ab8feeb0a2d135dc4f34035ea5f8566a3aece0f65e80c872bf92e33ac381","observation_id":"b3303931-8436-4e1c-b87e-27fcafcf2e7a","resolution":{"observed_at":"2026-08-12T18:28:01.433698Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.17644","last_updated":"2025-05-26T16:16:43Z","snapshot_observed_at":"2026-08-16T14:36:00.835576Z","submitted_at":"2024-01-31T07:52:48Z","title":"BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.17644","snapshot_observed_at":"2026-08-12T18:28:01.438721Z","title":"Towards efficient and reliable llm serving: A real-world workload study","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.438721Z"},"links":{"cited_paper":"/paper/2401.17644","citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:f4dbb014995d0a86e1b8965f639bd5966dd576ad06863895a51bbabce092656b","observation_id":"decb8d3c-e512-4ab7-a628-a57eb2b26359","resolution":{"observed_at":"2026-08-12T18:28:01.438721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.301897Z","title":"Gödel: Unified large-scale resource management and scheduling at bytedance","venue":null,"work_id":"4a82d043-5917-4e2d-9172-163c371c8009","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.443368Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:ddbb4e4c51b22748d758985d97634683fcd56fa528f288f78e2be27afedae40a","observation_id":"1838389c-2088-4752-ae94-a643ce2ca9f1","resolution":{"observed_at":"2026-08-12T18:28:02.306668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.285958Z","title":"Topology-aware gpu scheduling for learning workloads in cloud environments","venue":null,"work_id":"8887ccee-9027-426c-92fe-519e596e72aa","year":2017},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.447617Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:e487259fa749df207f4850bc7102c1b69068c520e10c34d4fa634282d83f5c46","observation_id":"686d5a79-898d-46f3-9724-e2a7a3495913","resolution":{"observed_at":"2026-08-12T18:28:02.291237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.270623Z","title":"Numa (non-uniform memory access): An overview: Numa becomes more common because memory controllers get close to execution units on microprocessors","venue":null,"work_id":"97d91b2b-b0a3-4b0e-b0fd-48499972a0b8","year":2013},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.452715Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:84602fa581c0d1878b1de8160e010b42c620254728d13dfd8d8883ecf69c10f9","observation_id":"cb7af90d-5efa-4522-b89b-27aadcf7a300","resolution":{"observed_at":"2026-08-12T18:28:02.275245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14527","last_updated":"2024-07-22T10:56:19Z","snapshot_observed_at":"2026-08-16T13:58:45.757126Z","submitted_at":"2024-04-22T18:56:18Z","title":"M\\'elange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14527","snapshot_observed_at":"2026-08-12T18:28:01.457638Z","title":"M\\’elange: Cost efficient large language model serving by exploiting gpu heterogeneity","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.457638Z"},"links":{"cited_paper":"/paper/2404.14527","citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:433964cd7b7005a608a2b39c664103e971298074bd680e94a49e522cc4e123ed","observation_id":"c2a87714-bfe2-4b13-bb56-e6dce973d287","resolution":{"observed_at":"2026-08-12T18:28:01.457638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.256437Z","title":"Fastertransformer","venue":null,"work_id":"1e1e0ab3-6aa3-4f5f-b8e7-e0ba02175bdb","year":2021},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.462536Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:fde2fe61e47a1360372c5d3f13a8281aa805a02ed9ea34a35c1049e2392cd536","observation_id":"b274c18f-8aa3-4049-8c3b-ef85184042f6","resolution":{"observed_at":"2026-08-12T18:28:02.261255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.466924Z","title":"Efficient memory management for large language model serving with pagedattention","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.466924Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:142417abcb3f5490057ba7c269c95139d0a2b7d2716c207105fd56df428934dd","observation_id":"d72f1f46-7198-4ed8-b7e6-929dac152349","resolution":{"observed_at":"2026-08-12T18:28:01.466924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.232619Z","title":null,"venue":null,"work_id":"87ea19c3-c5fd-45eb-8fd0-e8f5e02e0213","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.471222Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:d4b4f500c89a2cfbcd4477af5a2761edb438d1fbf444af6077ef0739c204657b","observation_id":"8059a009-b02e-4a5e-90ab-5b5462e55ffc","resolution":{"observed_at":"2026-08-12T18:28:02.236920Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.217200Z","title":"Huggingface text generation inference","venue":null,"work_id":"355f620a-13ed-48aa-93d9-b281891b87fa","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.476060Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:f3bd456f6d2407b6f49275e3bdf262da9bd3b38ac0547d82d7b445bcc2806d61","observation_id":"55bbb4f4-33c7-4400-9a72-8dedbf17fc3d","resolution":{"observed_at":"2026-08-12T18:28:02.223059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.200016Z","title":"Deepspeed inference","venue":null,"work_id":"aef0580e-fc43-4654-8c1a-3f44d3d6ef39","year":2022},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.481104Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:440b472e02c91cde9f960dc69a996494e14399ac8c9e079e7013425e3a4ad394","observation_id":"8cf4ed1a-5c32-4f90-8fc4-6828229232e4","resolution":{"observed_at":"2026-08-12T18:28:02.206174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.182548Z","title":"Tensorrt-llm","venue":null,"work_id":"60f37be1-fb12-4d1f-811a-b5432a0637c8","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.486113Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:dad8365d3ce9a7221af6387b4c9fcf43c9ab06403e2a5e29721f89fbdfff1f62","observation_id":"24929857-98da-480d-bb69-5d8d84af3978","resolution":{"observed_at":"2026-08-12T18:28:02.187894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15234","last_updated":"2025-07-23T10:11:55Z","snapshot_observed_at":"2026-08-16T14:32:00.300814Z","submitted_at":"2023-12-23T11:57:53Z","title":"Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.15234","snapshot_observed_at":"2026-08-12T18:28:01.491752Z","title":"To- wards efficient generative large language model serving: A survey from algorithms to systems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.491752Z"},"links":{"cited_paper":"/paper/2312.15234","citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:b9ef0e0214f86bf7406a3964e1fbb011f9809103185ce4aabebc668d1499b210","observation_id":"c33301c8-4278-4565-8476-81bae1b4adad","resolution":{"observed_at":"2026-08-12T18:28:01.491752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.164151Z","title":"Kubernetes topology manager moves to beta","venue":null,"work_id":"3a4ceabd-4220-43e7-bf78-234f9dec4c61","year":2020},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.497305Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:b7c98f5e4f12c95d61faa5bb954f86a246eeac0fb700a4519044cffb60683c08","observation_id":"35e1fa7c-29a6-48ae-8bf0-d7501de9fdf6","resolution":{"observed_at":"2026-08-12T18:28:02.170281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.145805Z","title":"Pod priority and preemption","venue":null,"work_id":"1b4055b5-fc6b-463f-b94b-308708a65e50","year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.502510Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:1c880efbb8a656ccace053e75954488daaa88b8d206cb3df2d973bf1d4827664","observation_id":"4a4273a1-70b4-4d0e-a9fb-d136a6eafbe2","resolution":{"observed_at":"2026-08-12T18:28:02.151560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.126434Z","title":"Godel scheduler: a unified scheduler for online and offline tasks","venue":null,"work_id":"dedbccfa-3cbb-449b-a069-0c22f6124cd2","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.507707Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:399fd99591112a6f94f11735d62595419bf084ae8ddcbef284795c6895de35eb","observation_id":"56641ff5-55ad-4d69-a0ed-47f142ee90b4","resolution":{"observed_at":"2026-08-12T18:28:02.132252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.105600Z","title":"Daemonset","venue":null,"work_id":"f0c20139-9cb4-43c3-a010-3937a6e56fe5","year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.513084Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:884912436f038a29326fab541803b73e42e5836e244f0e13b6247b00daa39f19","observation_id":"65743995-cd8e-4356-bc95-b0d38e2e1028","resolution":{"observed_at":"2026-08-12T18:28:02.112382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.087421Z","title":"Kubernetes without kubelet","venue":null,"work_id":"10bfeabf-2cc7-49a9-bd0f-145bf12f5655","year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.518354Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:54faf8e13c6fee813a2786c9727bb6a96b8db5c53e13c5139c0d9698312535a2","observation_id":"38571f2a-df86-4258-95d3-9677b53151bd","resolution":{"observed_at":"2026-08-12T18:28:02.092846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.070388Z","title":"Control topology management policies on a node","venue":null,"work_id":"0a598645-9138-4878-b6d6-ba3ad41b2544","year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.523308Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:7984b3d45ca043c3ed0539b116434053944b517ce80b4df1f79b9d29817afcbe","observation_id":"60eb0db1-4c97-4a32-ab61-2449af02ad9f","resolution":{"observed_at":"2026-08-12T18:28:02.076029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.055812Z","title":"Towards {GPU} utilization prediction for cloud deep learning","venue":null,"work_id":"cdef53ed-d886-429b-806e-e6564252d53f","year":2020},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.528634Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:e8fb5365b358fe6fe1f4926794dc861c588665e5d3b07c831e90aac232dd1b69","observation_id":"88a88c86-aed8-4e04-8cf0-847c994707ee","resolution":{"observed_at":"2026-08-12T18:28:02.060218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.041730Z","title":"Horus: Interference-aware and prediction-based scheduling in deep learning systems","venue":null,"work_id":"016a723a-9e4d-49bb-9fc8-f9d99fb6afef","year":2021},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.533781Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:0c875a0524ec977f9b991545aa814aecc0877a23d1aa7d4cb828a013b01cf9aa","observation_id":"b1a8bceb-c2a4-40f6-8530-0d45e9a5ee3f","resolution":{"observed_at":"2026-08-12T18:28:02.046202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.026824Z","title":"Beware of fragmentation: Scheduling {GPU-Sharing} workloads with fragmentation gradient descent","venue":null,"work_id":"548bbfc5-1c5d-400f-9398-bb3034e4dec2","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.539007Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:3df0cd61ad6f4d3a61ac0ac1215cf103f89bba7dd71280c76f0429335c2b1fad","observation_id":"925f7790-266f-4f84-b56e-a683690cf4c0","resolution":{"observed_at":"2026-08-12T18:28:02.031649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:02.011835Z","title":"{HiveD}: Sharing a {GPU} cluster for deep learning with guarantees","venue":null,"work_id":"065295df-f0b1-47e0-b103-00325982bfbc","year":2020},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.543941Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:d7ca1205604f9f36247638aaf68bdd12573a98b43afc9fb9730ec9cc76207a30","observation_id":"b9e132b9-40af-44c1-bf05-587c08e6e83b","resolution":{"observed_at":"2026-08-12T18:28:02.016593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.996920Z","title":"Supporting gpu sharing in cloud environments with a transparent runtime consolidation framework","venue":null,"work_id":"c348fa1c-630e-4ca0-a324-0ea4773ef0d4","year":2011},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.549509Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:f612c4ef8f0e8a88cd5fd1af6ab0e8e7892bb9eab2e0d9105162de2d8092f19f","observation_id":"a71a4c0e-220e-4f0c-b4f4-ff8a56479a6f","resolution":{"observed_at":"2026-08-12T18:28:02.001582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.983115Z","title":"Fine-grained gpu sharing primitives for deep learning applications","venue":null,"work_id":"d780bbbd-1a74-41a4-ab62-a7827d506112","year":2020},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.554580Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:b31daac7b9cfd93319f3cdfdde3a9636ae95225c0a8b2620ae37c1276e891609","observation_id":"748ddaea-6be4-49c5-ac6c-c15e16774fd0","resolution":{"observed_at":"2026-08-12T18:28:01.987696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.968746Z","title":"Gpushare: Fair-sharing middleware for gpu clouds","venue":null,"work_id":"dc38af48-b768-4747-a7ce-1c826771ff8f","year":2016},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.560039Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:f6f62766c87a6dc9e7d52753e0972aba8c1908ec79e533f4781a17e86305b3c2","observation_id":"8223a453-87fd-43c1-93e8-110117107960","resolution":{"observed_at":"2026-08-12T18:28:01.973274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.954845Z","title":"Nvidia cloud native technologies: Gpu sharing","venue":null,"work_id":"58d8181b-f9f3-4704-9c30-f22d55674bbe","year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.565070Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:ddbfd895e5319004c36ec63abecb63113dfd13f902f2fa15b7dcba5b7ddbc359","observation_id":"4506a771-c34a-4ba0-9258-99d951452b22","resolution":{"observed_at":"2026-08-12T18:28:01.959345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.940926Z","title":"Advanced features in ibm power8 systems","venue":null,"work_id":"6e2a44be-9d42-4deb-9a7c-394f1d4b44b8","year":2015},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.570281Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:0775bf447bf86cb02df9b2788af785e1abaad5366e3829995204763e312f0a87","observation_id":"4c09c855-110c-49b5-b9a1-3475a4e1f3c7","resolution":{"observed_at":"2026-08-12T18:28:01.945397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.927620Z","title":"Performance evaluation of the nvidia tesla p100: Our directive-based partitioning and pipelining vs","venue":null,"work_id":"bbb1154f-8706-432f-bbb3-598353f47147","year":2017},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.575734Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:1b8d5cde8e3f1fed6ea3d5c882175d32bacbec0b153eaff64d6fecf723de5738","observation_id":"1fb6f2b0-c3d3-45af-a511-191befec4d7f","resolution":{"observed_at":"2026-08-12T18:28:01.931958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.912332Z","title":"Topology-aware scheduling framework for microservice applications in cloud","venue":null,"work_id":"eed685d7-1e4d-49ac-994b-779e5f59c10a","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.580861Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:2ae247fc6bc87e1bd913b1fb1d306e75b0293f6bd6846ed965b8318821dd8612","observation_id":"deeb195f-5236-4900-8171-18ad8d71598f","resolution":{"observed_at":"2026-08-12T18:28:01.917790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.896628Z","title":"Katalyst core","venue":null,"work_id":"61d15826-7eb3-461d-bc9b-952c66ff7f5c","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.586803Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:9db62fa3017997a8315bf8c2ee204e26de248f3df6f698e35602e22ff395bcf3","observation_id":"dbf852d4-d6ba-44a7-a2ef-b31305b0d091","resolution":{"observed_at":"2026-08-12T18:28:01.901739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.878438Z","title":"Topology-aware resource allocation for data-intensive workloads","venue":null,"work_id":"0851dae1-72f3-48b7-b65a-3edc22c6dc21","year":2010},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.592516Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:933b71ab11e83ef5dc34834e91fb9bf2f71df809ce95ff60308851a883960e11","observation_id":"93263d25-13c5-437c-9a69-5016325fcf88","resolution":{"observed_at":"2026-08-12T18:28:01.884085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.860762Z","title":"Towards topology aware pre-emptive job scheduling with deep reinforcement learning","venue":null,"work_id":"bafaefcc-ee19-4e6c-aa28-2d182ab43e01","year":2020},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.597592Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:c55602d2df3bdfb525ff33d9205399ca8a4d1e7096ee7c840fa39ab15b4c1a12","observation_id":"6f4c2c72-2123-4979-afa7-c94df6b035d9","resolution":{"observed_at":"2026-08-12T18:28:01.865992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.842844Z","title":"Microsecond-scale preemption for concurrent {GPU- accelerated}{DNN} inferences","venue":null,"work_id":"8e1fc4d2-9077-470c-985b-945409727cc2","year":2022},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.603019Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:24060c3110af9ab4bfad3b1ec4e4ed9f7b6e12a2fed1e8240540027400ab2533","observation_id":"792f1e0a-0f86-4e97-a7ec-91a733a6150d","resolution":{"observed_at":"2026-08-12T18:28:01.849163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.825208Z","title":"Efficiently programming large language models using sglang","venue":null,"work_id":"3f7c707a-0d1d-425d-b63f-27cae37a9bad","year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.608437Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:8642084e0a31ea81f0c3d47adb097762033ad236c529182efff4d8c005de8988","observation_id":"cc0d2b9d-a5d2-457a-9492-ffaf42566c25","resolution":{"observed_at":"2026-08-12T18:28:01.830860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.614459Z","title":"Orca: A distributed serving system for {Transformer-Based} generative models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.614459Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:86b9137bfd403f87f170e7fea85ec37463d5676d1ecea265bed76df49d1b6aef","observation_id":"d548ec38-5922-4dd3-826e-3af1d996ebbd","resolution":{"observed_at":"2026-08-12T18:28:01.614459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.05920","last_updated":"2024-09-25T05:57:51Z","snapshot_observed_at":"2026-08-16T21:53:57.298060Z","submitted_at":"2023-05-10T06:17:50Z","title":"Fast Distributed Inference Serving for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.05920","snapshot_observed_at":"2026-08-12T18:28:01.620434Z","title":"Fast distributed inference serving for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.620434Z"},"links":{"cited_paper":"/paper/2305.05920","citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:ca77399e7e347ef793c832e12c9b2758164c88fcf0accbf77dfd85e9685164d3","observation_id":"9ff1a8a3-098a-48de-9bd6-1c601afba6c4","resolution":{"observed_at":"2026-08-12T18:28:01.620434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.796380Z","title":"Bert loses patience: Fast and robust inference with early exit","venue":null,"work_id":"3118af73-f094-46eb-8b91-cfbd3c83c536","year":2020},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.625813Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:ce4ae31a4f1fa542aa94b7d0e80adf9067b145062b25da4569d9441da1e9bb34","observation_id":"799a9f9b-e866-443b-a8f8-1e266cf3c4c9","resolution":{"observed_at":"2026-08-12T18:28:01.801769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.631009Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.631009Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:0a89b89db447aacfb589f9361f498ce8c90a7c52303033ba398c2db54f1df8e9","observation_id":"2ef5b5fb-8604-47f4-9f99-e25ed9042c7d","resolution":{"observed_at":"2026-08-12T18:28:01.631009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.636569Z","title":"Sparsegpt: Massive language models can be accurately pruned in one-shot","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.636569Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:bce880beb52116193c466c47a8d983e4cd1489f44269e04d71c364b71b2ad8b9","observation_id":"a7a7f4e6-21d2-45c6-bc1d-78087adeda08","resolution":{"observed_at":"2026-08-12T18:28:01.636569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:28:01.756077Z","title":"Awq: Activation-aware weight quantization for on-device llm compression and acceleration","venue":null,"work_id":"a5d191a3-4cf7-4cfc-90e2-a6e3d7b22a7f","year":2024},"citing_paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T18:28:01.641782Z"},"links":{"citing_paper":"/paper/2411.11560"},"observation_digest":"sha256:27fb76652b7d6ff45ae3a0f4ff2456cf629a4da77162593191ab29e3215be8ee","observation_id":"563b8ecf-5b3a-47ba-af67-92051c370136","resolution":{"observed_at":"2026-08-12T18:28:01.763314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.11560","last_updated":"2024-11-18T13:26:09Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-17T19:57:14.193076Z","submitted_at":"2024-11-18T13:26:09Z","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":42},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"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."}