{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:MZU4SVOT324ZWR5ODSJVHNLRPR","short_pith_number":"pith:MZU4SVOT","canonical_record":{"source":{"id":"1908.10834","kind":"arxiv","version":10},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-08-23T17:18:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"98bb0fc48cd4526fc115b3fc66d69c8b8b1b5292b418d7d95a43889cefd658d7","abstract_canon_sha256":"b6079654c7835be2846b56ec9e4dd11f2925412afeb52da7fe271e59a882278f"},"schema_version":"1.0"},"canonical_sha256":"6669c955d3deb99b47ae1c9353b5717c745bf131cd0ff7fd715effeb597ef5ee","source":{"kind":"arxiv","id":"1908.10834","version":10},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.10834","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"arxiv_version","alias_value":"1908.10834v10","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10834","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"pith_short_12","alias_value":"MZU4SVOT324Z","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"pith_short_16","alias_value":"MZU4SVOT324ZWR5O","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"pith_short_8","alias_value":"MZU4SVOT","created_at":"2026-07-05T01:34:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:MZU4SVOT324ZWR5ODSJVHNLRPR","target":"record","payload":{"canonical_record":{"source":{"id":"1908.10834","kind":"arxiv","version":10},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-08-23T17:18:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"98bb0fc48cd4526fc115b3fc66d69c8b8b1b5292b418d7d95a43889cefd658d7","abstract_canon_sha256":"b6079654c7835be2846b56ec9e4dd11f2925412afeb52da7fe271e59a882278f"},"schema_version":"1.0"},"canonical_sha256":"6669c955d3deb99b47ae1c9353b5717c745bf131cd0ff7fd715effeb597ef5ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:34:32.909904Z","signature_b64":"vh2q2Ay6MhNDwTF3VdRh2W0vbzExUwvu1pGL3GpG9JvoF2SqDGkNkNPgrm0z3eFW2v4lOtDxf8EMEsM6+CTOCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6669c955d3deb99b47ae1c9353b5717c745bf131cd0ff7fd715effeb597ef5ee","last_reissued_at":"2026-07-05T01:34:32.909525Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:34:32.909525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.10834","source_version":10,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:34:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7LCPW5tURlpPhNEGRapZE4MfzgNnps51IA7LQrjL45Whi80JtXBec+6Tilh/Mb/rkBI8bgbF0WfppypQ3uo4AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:48:07.899783Z"},"content_sha256":"8b40df66dd5fe80202df941d27c146336729f1999dbfc5e8c5e39b9d5a74ac9b","schema_version":"1.0","event_id":"sha256:8b40df66dd5fe80202df941d27c146336729f1999dbfc5e8c5e39b9d5a74ac9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:MZU4SVOT324ZWR5ODSJVHNLRPR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Ang Li, Antonino Tumeo, Chunshu Wu, Martin Herbordt, Pouya Haghi, Runbin Shi, Shuai Che, Steve Reinhardt, Tianqi Wang, Tong Geng, Yanfei Li","submitted_at":"2019-08-23T17:18:49Z","abstract_excerpt":"Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio. In many applications, however, information and their relationships are better expressed with graphs. Graph Convolutional Networks (GCNs) appear to be a promising approach to efficiently learn from graph data structures, having shown advantages in many critical applications. As with other deep learning modalities, hardware acceleration is critical. The challenge is that real-world graphs are often extremely large and unbalanced; this poses significant performance demands and design challenge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10834","kind":"arxiv","version":10},"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/1908.10834/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":"2026-07-05T01:34:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8AnDkkaf/MCZL/9ZqbiDkk+rjU/ErFj0s6Y7i1SWXUIFuSojbATt2lOhGc4LhUXlBf+SvtB1e3WCiqItXn13Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T13:48:07.900301Z"},"content_sha256":"303aab2d2c52282d83293df8f259aaafe1bda2714c4fad0fa74ed07f0ce0f01c","schema_version":"1.0","event_id":"sha256:303aab2d2c52282d83293df8f259aaafe1bda2714c4fad0fa74ed07f0ce0f01c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MZU4SVOT324ZWR5ODSJVHNLRPR/bundle.json","state_url":"https://pith.science/pith/MZU4SVOT324ZWR5ODSJVHNLRPR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MZU4SVOT324ZWR5ODSJVHNLRPR/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":"2026-07-31T13:48:07Z","links":{"resolver":"https://pith.science/pith/MZU4SVOT324ZWR5ODSJVHNLRPR","bundle":"https://pith.science/pith/MZU4SVOT324ZWR5ODSJVHNLRPR/bundle.json","state":"https://pith.science/pith/MZU4SVOT324ZWR5ODSJVHNLRPR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MZU4SVOT324ZWR5ODSJVHNLRPR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MZU4SVOT324ZWR5ODSJVHNLRPR","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":"b6079654c7835be2846b56ec9e4dd11f2925412afeb52da7fe271e59a882278f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-08-23T17:18:49Z","title_canon_sha256":"98bb0fc48cd4526fc115b3fc66d69c8b8b1b5292b418d7d95a43889cefd658d7"},"schema_version":"1.0","source":{"id":"1908.10834","kind":"arxiv","version":10}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.10834","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"arxiv_version","alias_value":"1908.10834v10","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10834","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"pith_short_12","alias_value":"MZU4SVOT324Z","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"pith_short_16","alias_value":"MZU4SVOT324ZWR5O","created_at":"2026-07-05T01:34:32Z"},{"alias_kind":"pith_short_8","alias_value":"MZU4SVOT","created_at":"2026-07-05T01:34:32Z"}],"graph_snapshots":[{"event_id":"sha256:303aab2d2c52282d83293df8f259aaafe1bda2714c4fad0fa74ed07f0ce0f01c","target":"graph","created_at":"2026-07-05T01:34:32Z","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/1908.10834/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio. In many applications, however, information and their relationships are better expressed with graphs. Graph Convolutional Networks (GCNs) appear to be a promising approach to efficiently learn from graph data structures, having shown advantages in many critical applications. As with other deep learning modalities, hardware acceleration is critical. The challenge is that real-world graphs are often extremely large and unbalanced; this poses significant performance demands and design challenge","authors_text":"Ang Li, Antonino Tumeo, Chunshu Wu, Martin Herbordt, Pouya Haghi, Runbin Shi, Shuai Che, Steve Reinhardt, Tianqi Wang, Tong Geng, Yanfei Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-08-23T17:18:49Z","title":"AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10834","kind":"arxiv","version":10},"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":"sha256:8b40df66dd5fe80202df941d27c146336729f1999dbfc5e8c5e39b9d5a74ac9b","target":"record","created_at":"2026-07-05T01:34:32Z","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":"b6079654c7835be2846b56ec9e4dd11f2925412afeb52da7fe271e59a882278f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-08-23T17:18:49Z","title_canon_sha256":"98bb0fc48cd4526fc115b3fc66d69c8b8b1b5292b418d7d95a43889cefd658d7"},"schema_version":"1.0","source":{"id":"1908.10834","kind":"arxiv","version":10}},"canonical_sha256":"6669c955d3deb99b47ae1c9353b5717c745bf131cd0ff7fd715effeb597ef5ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6669c955d3deb99b47ae1c9353b5717c745bf131cd0ff7fd715effeb597ef5ee","first_computed_at":"2026-07-05T01:34:32.909525Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:34:32.909525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vh2q2Ay6MhNDwTF3VdRh2W0vbzExUwvu1pGL3GpG9JvoF2SqDGkNkNPgrm0z3eFW2v4lOtDxf8EMEsM6+CTOCw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:34:32.909904Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.10834","source_kind":"arxiv","source_version":10}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b40df66dd5fe80202df941d27c146336729f1999dbfc5e8c5e39b9d5a74ac9b","sha256:303aab2d2c52282d83293df8f259aaafe1bda2714c4fad0fa74ed07f0ce0f01c"],"state_sha256":"2c4e47a00fd1fd0663c21b1df06f4d6de1b167010ddfdc79ac2e14f2e95f19f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kYJlMNLpoY71blBvJglkdlOZdsLnmdYeC3fKwJny1Q+hMraxNSPY3rW/P/Fne6RDMPtRUIywpyNNLhspNokxAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T13:48:07.905853Z","bundle_sha256":"10a7ed6b0c4b2b2c6b1d9fa83a53d99af3b3310d49d4c237b0dcfd1045c18aa1"}}