{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:KWZB5YUR24P3XY6QPWTJCRJSCM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"94e39bbc3cbe5de3c9a0b3cc7760dc13b40b83c44aebd77425942669c346928c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-17T00:41:43Z","title_canon_sha256":"4336daff5ad76db126f06363f8d73908281fa2018aa66b07795c45eb975b8437"},"schema_version":"1.0","source":{"id":"2110.08688","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.08688","created_at":"2026-07-05T03:23:19Z"},{"alias_kind":"arxiv_version","alias_value":"2110.08688v1","created_at":"2026-07-05T03:23:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.08688","created_at":"2026-07-05T03:23:19Z"},{"alias_kind":"pith_short_12","alias_value":"KWZB5YUR24P3","created_at":"2026-07-05T03:23:19Z"},{"alias_kind":"pith_short_16","alias_value":"KWZB5YUR24P3XY6Q","created_at":"2026-07-05T03:23:19Z"},{"alias_kind":"pith_short_8","alias_value":"KWZB5YUR","created_at":"2026-07-05T03:23:19Z"}],"graph_snapshots":[{"event_id":"sha256:bc790869d42620d23fd5a121721d15d468bed9033dd73c5c759321a650d8cbe3","target":"graph","created_at":"2026-07-05T03:23:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2110.08688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more. Recent work has shown that, for the graphs used in the machine learning community, communication becomes a bottleneck and scaling is blocked outside of the single machine regime. Thus, we propose MG-GCN, a multi-GPU GCN training framework taking advantage of the high-speed communication links between the GPUs present in multi-GPU systems. MG-GCN employs multiple High-Performance Computing optimizations, including efficient re-use of m","authors_text":"Kaan Sancak, Muhammed Fatih Bal{\\i}n, \\\"Umit V. \\c{C}ataly\\\"urek","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-17T00:41:43Z","title":"MG-GCN: Scalable Multi-GPU GCN Training Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.08688","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:23a8e2f3bf1a91455974004e9f586ba62fa25785172e761d3d8cde8b95625750","target":"record","created_at":"2026-07-05T03:23:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"94e39bbc3cbe5de3c9a0b3cc7760dc13b40b83c44aebd77425942669c346928c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-17T00:41:43Z","title_canon_sha256":"4336daff5ad76db126f06363f8d73908281fa2018aa66b07795c45eb975b8437"},"schema_version":"1.0","source":{"id":"2110.08688","kind":"arxiv","version":1}},"canonical_sha256":"55b21ee291d71fbbe3d07da6914532132ff687674cd3f582ad7775693bb880ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55b21ee291d71fbbe3d07da6914532132ff687674cd3f582ad7775693bb880ce","first_computed_at":"2026-07-05T03:23:19.413304Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:23:19.413304Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OWPhzuUZT6Y3hzG7YMcg+ym3lNTfT/x8TjbCbZeSDDWZxZ4WTi/Lrq/LU7/VlOnEYhd/eNXBcF94mJ66fXg/AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:23:19.413685Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.08688","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:23a8e2f3bf1a91455974004e9f586ba62fa25785172e761d3d8cde8b95625750","sha256:bc790869d42620d23fd5a121721d15d468bed9033dd73c5c759321a650d8cbe3"],"state_sha256":"db347fe06841fb2babf0deeea4b4f55dc965c464eb02877a8b4b9cdd47190246"}