{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:4TS6E76OXSTN7OCBYELV347MSP","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":"7d6cc047872dd99b39177093199a47ce545d5ebe20d0b823b2c1782a0020dc8a","cross_cats_sorted":["cs.DC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-05T20:41:06Z","title_canon_sha256":"7000e3781db24a567f07715af4592fc898edcea975e5e5146893cf8ecb69de5b"},"schema_version":"1.0","source":{"id":"1909.02625","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.02625","created_at":"2026-07-05T03:49:36Z"},{"alias_kind":"arxiv_version","alias_value":"1909.02625v3","created_at":"2026-07-05T03:49:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02625","created_at":"2026-07-05T03:49:36Z"},{"alias_kind":"pith_short_12","alias_value":"4TS6E76OXSTN","created_at":"2026-07-05T03:49:36Z"},{"alias_kind":"pith_short_16","alias_value":"4TS6E76OXSTN7OCB","created_at":"2026-07-05T03:49:36Z"},{"alias_kind":"pith_short_8","alias_value":"4TS6E76O","created_at":"2026-07-05T03:49:36Z"}],"graph_snapshots":[{"event_id":"sha256:04f48c75c69770415a0bec1e1751633df2d1356252152239c62010d1f326369a","target":"graph","created_at":"2026-07-05T03:49:36Z","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/1909.02625/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Training the deep convolutional neural network for computer vision problems is slow and inefficient, especially when it is large and distributed across multiple devices. The inefficiency is caused by the backpropagation algorithm's forward locking, backward locking, and update locking problems. Existing solutions for acceleration either can only handle one locking problem or lead to severe accuracy loss or memory inefficiency. Moreover, none of them consider the straggler problem among devices. In this paper, we propose Layer-wise Staleness and a novel efficient training algorithm, Diversely S","authors_text":"An Xu, Heng Huang, Zhouyuan Huo","cross_cats":["cs.DC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-05T20:41:06Z","title":"On the Acceleration of Deep Learning Model Parallelism with Staleness"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02625","kind":"arxiv","version":3},"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:c968cec3cd783925486011eb6dbe508164f3678d98c44ad7a53bfd25933bccd1","target":"record","created_at":"2026-07-05T03:49:36Z","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":"7d6cc047872dd99b39177093199a47ce545d5ebe20d0b823b2c1782a0020dc8a","cross_cats_sorted":["cs.DC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-05T20:41:06Z","title_canon_sha256":"7000e3781db24a567f07715af4592fc898edcea975e5e5146893cf8ecb69de5b"},"schema_version":"1.0","source":{"id":"1909.02625","kind":"arxiv","version":3}},"canonical_sha256":"e4e5e27fcebca6dfb841c1175df3ec93f35a49d6f64ad22eefe4ea260e7806bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e4e5e27fcebca6dfb841c1175df3ec93f35a49d6f64ad22eefe4ea260e7806bc","first_computed_at":"2026-07-05T03:49:36.559767Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:49:36.559767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Nq47zbbg54hn5VE7Fz+M72CScvkodS0rT3DS6Ts9vCTj771QvSFBEhHvNyFPIsPNf6N+4EvTI7C4lg5ItE6pCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:49:36.560179Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.02625","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c968cec3cd783925486011eb6dbe508164f3678d98c44ad7a53bfd25933bccd1","sha256:04f48c75c69770415a0bec1e1751633df2d1356252152239c62010d1f326369a"],"state_sha256":"54f8e08c90bded5bf5fdae5e39623133ab3ccf64341f15764d96ad7f3ae362ee"}