{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FWDVLCKHXNB4L5ENFFZCTPM4QT","short_pith_number":"pith:FWDVLCKH","canonical_record":{"source":{"id":"2412.12218","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T01:57:53Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"8fe10067a7de6ecc66c0b2323e9e872592b23414b612af438e457394b9c8ef84","abstract_canon_sha256":"b3cfcc9998feb0adab913d96ea4fd91d21ef51f35f7b1917c15efef21abb0184"},"schema_version":"1.0"},"canonical_sha256":"2d87558947bb43c5f48d297229bd9c84c94324c7290a6ffc02402a9aff4cbc3d","source":{"kind":"arxiv","id":"2412.12218","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.12218","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"arxiv_version","alias_value":"2412.12218v2","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12218","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"pith_short_12","alias_value":"FWDVLCKHXNB4","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"pith_short_16","alias_value":"FWDVLCKHXNB4L5EN","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"pith_short_8","alias_value":"FWDVLCKH","created_at":"2026-07-05T10:18:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FWDVLCKHXNB4L5ENFFZCTPM4QT","target":"record","payload":{"canonical_record":{"source":{"id":"2412.12218","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T01:57:53Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"8fe10067a7de6ecc66c0b2323e9e872592b23414b612af438e457394b9c8ef84","abstract_canon_sha256":"b3cfcc9998feb0adab913d96ea4fd91d21ef51f35f7b1917c15efef21abb0184"},"schema_version":"1.0"},"canonical_sha256":"2d87558947bb43c5f48d297229bd9c84c94324c7290a6ffc02402a9aff4cbc3d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:46.941324Z","signature_b64":"dFpwYd6ZBOXXTMapAnviG/xk0EnNqVMxRoRDbAuPxBmb/NoEyOKgFvvNvFaCOp4lBBHI8fx0XUr0jGdr9gMEDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d87558947bb43c5f48d297229bd9c84c94324c7290a6ffc02402a9aff4cbc3d","last_reissued_at":"2026-07-05T10:18:46.940823Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:46.940823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.12218","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:18:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w3tDstyokafbW+zcwQt5y8d+GPXP+CV/piQ3bmrUahypcB3FixXm8zeuWpZpaOxWE86HB3Reo+itZ3I7AnaqBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:44:51.870262Z"},"content_sha256":"4935a61b6b221bc75364621ff2228ecbdaa27de5893feeca346b27442b9fa3b2","schema_version":"1.0","event_id":"sha256:4935a61b6b221bc75364621ff2228ecbdaa27de5893feeca346b27442b9fa3b2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FWDVLCKHXNB4L5ENFFZCTPM4QT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Sparse Graph Neural Networks with Tensor Core Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Ka Wai Wu","submitted_at":"2024-12-16T01:57:53Z","abstract_excerpt":"Graph neural networks (GNNs) have seen extensive application in domains such as social networks, bioinformatics, and recommendation systems. However, the irregularity and sparsity of graph data challenge traditional computing methods, which are insufficient to meet the performance demands of GNNs. Recent research has explored parallel acceleration using CUDA Cores and Tensor Cores, but significant challenges persist: (1) kernel fusion leads to false high utilization, failing to treat CUDA and Tensor Cores as independent resources, and (2) heterogeneous cores have distinct computation preferenc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12218","kind":"arxiv","version":2},"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/2412.12218/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:18:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ffL2rqUpA3h+O3r/LvWcZfTZaDVVUV7sVzTSqHfoaTPRwvh9PrnPuqxSjh668lqiKK11QnoHV8oezWJkZMivBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:44:51.870785Z"},"content_sha256":"8758bbf191c5a9e3157db557c8545faa94a1ba27c6d5cca430d599777909543f","schema_version":"1.0","event_id":"sha256:8758bbf191c5a9e3157db557c8545faa94a1ba27c6d5cca430d599777909543f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FWDVLCKHXNB4L5ENFFZCTPM4QT/bundle.json","state_url":"https://pith.science/pith/FWDVLCKHXNB4L5ENFFZCTPM4QT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FWDVLCKHXNB4L5ENFFZCTPM4QT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-15T20:44:51Z","links":{"resolver":"https://pith.science/pith/FWDVLCKHXNB4L5ENFFZCTPM4QT","bundle":"https://pith.science/pith/FWDVLCKHXNB4L5ENFFZCTPM4QT/bundle.json","state":"https://pith.science/pith/FWDVLCKHXNB4L5ENFFZCTPM4QT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FWDVLCKHXNB4L5ENFFZCTPM4QT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FWDVLCKHXNB4L5ENFFZCTPM4QT","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":"b3cfcc9998feb0adab913d96ea4fd91d21ef51f35f7b1917c15efef21abb0184","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T01:57:53Z","title_canon_sha256":"8fe10067a7de6ecc66c0b2323e9e872592b23414b612af438e457394b9c8ef84"},"schema_version":"1.0","source":{"id":"2412.12218","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.12218","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"arxiv_version","alias_value":"2412.12218v2","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12218","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"pith_short_12","alias_value":"FWDVLCKHXNB4","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"pith_short_16","alias_value":"FWDVLCKHXNB4L5EN","created_at":"2026-07-05T10:18:46Z"},{"alias_kind":"pith_short_8","alias_value":"FWDVLCKH","created_at":"2026-07-05T10:18:46Z"}],"graph_snapshots":[{"event_id":"sha256:8758bbf191c5a9e3157db557c8545faa94a1ba27c6d5cca430d599777909543f","target":"graph","created_at":"2026-07-05T10:18:46Z","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/2412.12218/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph neural networks (GNNs) have seen extensive application in domains such as social networks, bioinformatics, and recommendation systems. However, the irregularity and sparsity of graph data challenge traditional computing methods, which are insufficient to meet the performance demands of GNNs. Recent research has explored parallel acceleration using CUDA Cores and Tensor Cores, but significant challenges persist: (1) kernel fusion leads to false high utilization, failing to treat CUDA and Tensor Cores as independent resources, and (2) heterogeneous cores have distinct computation preferenc","authors_text":"Ka Wai Wu","cross_cats":["cs.AR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T01:57:53Z","title":"Accelerating Sparse Graph Neural Networks with Tensor Core Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12218","kind":"arxiv","version":2},"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":"sha256:4935a61b6b221bc75364621ff2228ecbdaa27de5893feeca346b27442b9fa3b2","target":"record","created_at":"2026-07-05T10:18:46Z","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":"b3cfcc9998feb0adab913d96ea4fd91d21ef51f35f7b1917c15efef21abb0184","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-16T01:57:53Z","title_canon_sha256":"8fe10067a7de6ecc66c0b2323e9e872592b23414b612af438e457394b9c8ef84"},"schema_version":"1.0","source":{"id":"2412.12218","kind":"arxiv","version":2}},"canonical_sha256":"2d87558947bb43c5f48d297229bd9c84c94324c7290a6ffc02402a9aff4cbc3d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2d87558947bb43c5f48d297229bd9c84c94324c7290a6ffc02402a9aff4cbc3d","first_computed_at":"2026-07-05T10:18:46.940823Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:18:46.940823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dFpwYd6ZBOXXTMapAnviG/xk0EnNqVMxRoRDbAuPxBmb/NoEyOKgFvvNvFaCOp4lBBHI8fx0XUr0jGdr9gMEDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:18:46.941324Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.12218","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4935a61b6b221bc75364621ff2228ecbdaa27de5893feeca346b27442b9fa3b2","sha256:8758bbf191c5a9e3157db557c8545faa94a1ba27c6d5cca430d599777909543f"],"state_sha256":"e5d44e2837bc98a9156c6bf105d6ec8f4a4be7740fc2f19dbbccf7d8251ed186"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HHkHQxnBMby6THsp4nf2YMPN7cKbpQlPf/+zdf0lRKUO3LvsxoH73x7ksYhIIYBqmTo62DMqSxYfvWCRqD/tAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T20:44:51.876040Z","bundle_sha256":"9f152ff9b9738cd58f46a3dad09b2b5ad076ef8b1aa59dfe0fd50f3757004427"}}