{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:23QNCODH577IUWDFZU4YX4M5NI","short_pith_number":"pith:23QNCODH","schema_version":"1.0","canonical_sha256":"d6e0d13867effe8a5865cd398bf19d6a23899128b69bd8c8be0ae72d5a18aaf6","source":{"kind":"arxiv","id":"2504.09307","version":1},"attestation_state":"computed","paper":{"title":"Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Brian Coutinho, Christina Delimitrou, Hiwot Tadese Kassa, Louis Feng, Mingyu Liang, Wenyin Fu","submitted_at":"2025-04-12T18:43:24Z","abstract_excerpt":"Training LLMs in distributed environments presents significant challenges due to the complexity of model execution, deployment systems, and the vast space of configurable strategies. Although various optimization techniques exist, achieving high efficiency in practice remains difficult. Accurate performance models that effectively characterize and predict a model's behavior are essential for guiding optimization efforts and system-level studies. We propose Lumos, a trace-driven performance modeling and estimation toolkit for large-scale LLM training, designed to accurately capture and predict "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.09307","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DC","submitted_at":"2025-04-12T18:43:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1eb2eae472b5f9542ba2ff5fa89cecc2f8e14b11bf5d6fc7a3bbd2bcd41d693d","abstract_canon_sha256":"d8e9bdff9b10b727667de38a68f4822782d16afa4e3cdcede346a51071ee33bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:48:35.077258Z","signature_b64":"1c3/suqvt8SxJIxsIQn7dtnouYf2ZMvqLQE4bVL4oOPwmWp246OW/epVKIyWMvGQXFpF5QGdguW0PnjFiPcsBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6e0d13867effe8a5865cd398bf19d6a23899128b69bd8c8be0ae72d5a18aaf6","last_reissued_at":"2026-07-05T10:48:35.076795Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:48:35.076795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Brian Coutinho, Christina Delimitrou, Hiwot Tadese Kassa, Louis Feng, Mingyu Liang, Wenyin Fu","submitted_at":"2025-04-12T18:43:24Z","abstract_excerpt":"Training LLMs in distributed environments presents significant challenges due to the complexity of model execution, deployment systems, and the vast space of configurable strategies. Although various optimization techniques exist, achieving high efficiency in practice remains difficult. Accurate performance models that effectively characterize and predict a model's behavior are essential for guiding optimization efforts and system-level studies. We propose Lumos, a trace-driven performance modeling and estimation toolkit for large-scale LLM training, designed to accurately capture and predict "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.09307","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.09307/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.09307","created_at":"2026-07-05T10:48:35.076845+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.09307v1","created_at":"2026-07-05T10:48:35.076845+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.09307","created_at":"2026-07-05T10:48:35.076845+00:00"},{"alias_kind":"pith_short_12","alias_value":"23QNCODH577I","created_at":"2026-07-05T10:48:35.076845+00:00"},{"alias_kind":"pith_short_16","alias_value":"23QNCODH577IUWDF","created_at":"2026-07-05T10:48:35.076845+00:00"},{"alias_kind":"pith_short_8","alias_value":"23QNCODH","created_at":"2026-07-05T10:48:35.076845+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17164","citing_title":"Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11333","citing_title":"MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17164","citing_title":"Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11333","citing_title":"MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces","ref_index":75,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI","json":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI.json","graph_json":"https://pith.science/api/pith-number/23QNCODH577IUWDFZU4YX4M5NI/graph.json","events_json":"https://pith.science/api/pith-number/23QNCODH577IUWDFZU4YX4M5NI/events.json","paper":"https://pith.science/paper/23QNCODH"},"agent_actions":{"view_html":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI","download_json":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI.json","view_paper":"https://pith.science/paper/23QNCODH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.09307&json=true","fetch_graph":"https://pith.science/api/pith-number/23QNCODH577IUWDFZU4YX4M5NI/graph.json","fetch_events":"https://pith.science/api/pith-number/23QNCODH577IUWDFZU4YX4M5NI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI/action/storage_attestation","attest_author":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI/action/author_attestation","sign_citation":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI/action/citation_signature","submit_replication":"https://pith.science/pith/23QNCODH577IUWDFZU4YX4M5NI/action/replication_record"}},"created_at":"2026-07-05T10:48:35.076845+00:00","updated_at":"2026-07-05T10:48:35.076845+00:00"}