{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZIAKOAF7IPBT4NM5PBM2QHTN4A","short_pith_number":"pith:ZIAKOAF7","schema_version":"1.0","canonical_sha256":"ca00a700bf43c33e359d7859a81e6de01737f6052175d7e32cd39a0446cb5988","source":{"kind":"arxiv","id":"2605.31500","version":1},"attestation_state":"computed","paper":{"title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexey Boykov, Andrey Dolgovyazov, Daniil Krasylnikov, Daria Fomina, Fedor Velikonivtsev, Vyacheslav Zhdanovskiy","submitted_at":"2026-05-29T16:22:45Z","abstract_excerpt":"Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general message passing, but complex layers often materialize edge-wise intermediates, increasing memory traffic and limiting scalability on large graphs. We take an I/O- and arithmetic-intensity--centric view and show that widely used layers fall into three kernel families: SpMM-based convolutions, reduction-based aggregations, and attention-based layers (GATv2/Graph Transformer). For each family, we develop GPU kernels that reduce data movement, impro"},"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":"2605.31500","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T16:22:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2705ba8426e4285292edf5b701b025d26be767477a2b8cd03b290d52fe2837db","abstract_canon_sha256":"15bee154542a371b6b348ee47c390c8139c941b14803e54c0a2fc3fb9b477f87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T02:04:09.526166Z","signature_b64":"7rPcy9wM1NNu0vj3KcWVZKe/fRFoYNEP9cKMur+tLejNiC7O6MwjkKttj/nBGOu18qMoGrG53EdqblyTKjAbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca00a700bf43c33e359d7859a81e6de01737f6052175d7e32cd39a0446cb5988","last_reissued_at":"2026-06-01T02:04:09.525307Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T02:04:09.525307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexey Boykov, Andrey Dolgovyazov, Daniil Krasylnikov, Daria Fomina, Fedor Velikonivtsev, Vyacheslav Zhdanovskiy","submitted_at":"2026-05-29T16:22:45Z","abstract_excerpt":"Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general message passing, but complex layers often materialize edge-wise intermediates, increasing memory traffic and limiting scalability on large graphs. We take an I/O- and arithmetic-intensity--centric view and show that widely used layers fall into three kernel families: SpMM-based convolutions, reduction-based aggregations, and attention-based layers (GATv2/Graph Transformer). For each family, we develop GPU kernels that reduce data movement, impro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31500","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/2605.31500/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":"2605.31500","created_at":"2026-06-01T02:04:09.525459+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31500v1","created_at":"2026-06-01T02:04:09.525459+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31500","created_at":"2026-06-01T02:04:09.525459+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZIAKOAF7IPBT","created_at":"2026-06-01T02:04:09.525459+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZIAKOAF7IPBT4NM5","created_at":"2026-06-01T02:04:09.525459+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZIAKOAF7","created_at":"2026-06-01T02:04:09.525459+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/ZIAKOAF7IPBT4NM5PBM2QHTN4A","json":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A.json","graph_json":"https://pith.science/api/pith-number/ZIAKOAF7IPBT4NM5PBM2QHTN4A/graph.json","events_json":"https://pith.science/api/pith-number/ZIAKOAF7IPBT4NM5PBM2QHTN4A/events.json","paper":"https://pith.science/paper/ZIAKOAF7"},"agent_actions":{"view_html":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A","download_json":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A.json","view_paper":"https://pith.science/paper/ZIAKOAF7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31500&json=true","fetch_graph":"https://pith.science/api/pith-number/ZIAKOAF7IPBT4NM5PBM2QHTN4A/graph.json","fetch_events":"https://pith.science/api/pith-number/ZIAKOAF7IPBT4NM5PBM2QHTN4A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A/action/storage_attestation","attest_author":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A/action/author_attestation","sign_citation":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A/action/citation_signature","submit_replication":"https://pith.science/pith/ZIAKOAF7IPBT4NM5PBM2QHTN4A/action/replication_record"}},"created_at":"2026-06-01T02:04:09.525459+00:00","updated_at":"2026-06-01T02:04:09.525459+00:00"}