{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6QG7FQOF2KH5LFYLUTSXH62OTL","short_pith_number":"pith:6QG7FQOF","schema_version":"1.0","canonical_sha256":"f40df2c1c5d28fd5970ba4e573fb4e9ade6047cdb74e2c2830857dc186d44035","source":{"kind":"arxiv","id":"2203.10428","version":1},"attestation_state":"computed","paper":{"title":"PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anastasios Kyrillidis, Cameron R. Wolfe, Cheng Wan, Nam Sung Kim, Yingyan Lin, Youjie Li","submitted_at":"2022-03-20T02:08:03Z","abstract_excerpt":"Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple accelerators such that each accelerator is able to hold a partitioned subgraph. However, distributed GCN training incurs prohibitive overhead of communicating node features and feature gradients among partitions for every GCN layer during each training iteration, limiting the achievable training efficiency and model scalability. To this end, we propose PipeGCN, a simple yet effective scheme that hides the communicatio"},"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":"2203.10428","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-20T02:08:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0ef381dcadef0e17a063d25414b756ac51efb74f1e8f65499db1c1cc3e910340","abstract_canon_sha256":"2a82bfca01a8a9eaaf74587f6745a06d2dd7a1171d5435a4d59428fb4ba37d4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:52.827656Z","signature_b64":"vVuuyuzy7mcyjQoOwJG/sHbiNPETkt0AAKBAertEhDuN7Oj4eHQlh2oqkvLCBHf77hus8GBwDAce5ZbyetL/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f40df2c1c5d28fd5970ba4e573fb4e9ade6047cdb74e2c2830857dc186d44035","last_reissued_at":"2026-07-05T04:06:52.827189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:52.827189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Anastasios Kyrillidis, Cameron R. Wolfe, Cheng Wan, Nam Sung Kim, Yingyan Lin, Youjie Li","submitted_at":"2022-03-20T02:08:03Z","abstract_excerpt":"Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple accelerators such that each accelerator is able to hold a partitioned subgraph. However, distributed GCN training incurs prohibitive overhead of communicating node features and feature gradients among partitions for every GCN layer during each training iteration, limiting the achievable training efficiency and model scalability. To this end, we propose PipeGCN, a simple yet effective scheme that hides the communicatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.10428","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/2203.10428/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":"2203.10428","created_at":"2026-07-05T04:06:52.827245+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.10428v1","created_at":"2026-07-05T04:06:52.827245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10428","created_at":"2026-07-05T04:06:52.827245+00:00"},{"alias_kind":"pith_short_12","alias_value":"6QG7FQOF2KH5","created_at":"2026-07-05T04:06:52.827245+00:00"},{"alias_kind":"pith_short_16","alias_value":"6QG7FQOF2KH5LFYL","created_at":"2026-07-05T04:06:52.827245+00:00"},{"alias_kind":"pith_short_8","alias_value":"6QG7FQOF","created_at":"2026-07-05T04:06:52.827245+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02916","citing_title":"GreenGNN: Energy-Aware Windowed Communication Optimization for Distributed GNN Training","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02651","citing_title":"Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL","json":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL.json","graph_json":"https://pith.science/api/pith-number/6QG7FQOF2KH5LFYLUTSXH62OTL/graph.json","events_json":"https://pith.science/api/pith-number/6QG7FQOF2KH5LFYLUTSXH62OTL/events.json","paper":"https://pith.science/paper/6QG7FQOF"},"agent_actions":{"view_html":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL","download_json":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL.json","view_paper":"https://pith.science/paper/6QG7FQOF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.10428&json=true","fetch_graph":"https://pith.science/api/pith-number/6QG7FQOF2KH5LFYLUTSXH62OTL/graph.json","fetch_events":"https://pith.science/api/pith-number/6QG7FQOF2KH5LFYLUTSXH62OTL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL/action/storage_attestation","attest_author":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL/action/author_attestation","sign_citation":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL/action/citation_signature","submit_replication":"https://pith.science/pith/6QG7FQOF2KH5LFYLUTSXH62OTL/action/replication_record"}},"created_at":"2026-07-05T04:06:52.827245+00:00","updated_at":"2026-07-05T04:06:52.827245+00:00"}