{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:57LBHDZVNXBMGG4LOM6KJEUAJY","short_pith_number":"pith:57LBHDZV","schema_version":"1.0","canonical_sha256":"efd6138f356dc2c31b8b733ca492804e0219d1fd29492e4f06186e3d4a7f96d7","source":{"kind":"arxiv","id":"2301.13799","version":1},"attestation_state":"computed","paper":{"title":"Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Alessandro Ottino, Christopher W. F. Parsonson, Georgios Zervas, Zacharaya Shabka","submitted_at":"2023-01-31T17:41:07Z","abstract_excerpt":"From natural language processing to genome sequencing, large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and instead must be distributed across multiple devices. This has motivated the research of new compute and network systems capable of handling such tasks. In particular, recent work has focused on developing management schemes which decide how to allocate distributed resources such that some overall objective, such as minimising the job completion time (JCT), is optimised. However, suc"},"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":"2301.13799","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-31T17:41:07Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"c654fd4c0d137e26410a92fd64d4090dd749a01d381c5854f74cb517100f3d13","abstract_canon_sha256":"e7bd92d878b039d22f44157a510b6fd87b98d60b27a5866bb5dc526f0346edff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:37:19.104621Z","signature_b64":"OVu293iFojTNp+pxkklQAkRnzUutfs/v8WF9l5s5szs9WvEi/YFGrvlBuolFt/Vqplq1DhEY/Q6sZxg3D5pmDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efd6138f356dc2c31b8b733ca492804e0219d1fd29492e4f06186e3d4a7f96d7","last_reissued_at":"2026-07-05T05:37:19.104148Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:37:19.104148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Alessandro Ottino, Christopher W. F. Parsonson, Georgios Zervas, Zacharaya Shabka","submitted_at":"2023-01-31T17:41:07Z","abstract_excerpt":"From natural language processing to genome sequencing, large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and instead must be distributed across multiple devices. This has motivated the research of new compute and network systems capable of handling such tasks. In particular, recent work has focused on developing management schemes which decide how to allocate distributed resources such that some overall objective, such as minimising the job completion time (JCT), is optimised. However, suc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13799","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/2301.13799/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":"2301.13799","created_at":"2026-07-05T05:37:19.104212+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.13799v1","created_at":"2026-07-05T05:37:19.104212+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13799","created_at":"2026-07-05T05:37:19.104212+00:00"},{"alias_kind":"pith_short_12","alias_value":"57LBHDZVNXBM","created_at":"2026-07-05T05:37:19.104212+00:00"},{"alias_kind":"pith_short_16","alias_value":"57LBHDZVNXBMGG4L","created_at":"2026-07-05T05:37:19.104212+00:00"},{"alias_kind":"pith_short_8","alias_value":"57LBHDZV","created_at":"2026-07-05T05:37:19.104212+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY","json":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY.json","graph_json":"https://pith.science/api/pith-number/57LBHDZVNXBMGG4LOM6KJEUAJY/graph.json","events_json":"https://pith.science/api/pith-number/57LBHDZVNXBMGG4LOM6KJEUAJY/events.json","paper":"https://pith.science/paper/57LBHDZV"},"agent_actions":{"view_html":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY","download_json":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY.json","view_paper":"https://pith.science/paper/57LBHDZV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.13799&json=true","fetch_graph":"https://pith.science/api/pith-number/57LBHDZVNXBMGG4LOM6KJEUAJY/graph.json","fetch_events":"https://pith.science/api/pith-number/57LBHDZVNXBMGG4LOM6KJEUAJY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY/action/storage_attestation","attest_author":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY/action/author_attestation","sign_citation":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY/action/citation_signature","submit_replication":"https://pith.science/pith/57LBHDZVNXBMGG4LOM6KJEUAJY/action/replication_record"}},"created_at":"2026-07-05T05:37:19.104212+00:00","updated_at":"2026-07-05T05:37:19.104212+00:00"}