{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NOOOAGJC7737FRV6TZFZTS4TQM","short_pith_number":"pith:NOOOAGJC","canonical_record":{"source":{"id":"1911.04610","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-24T00:13:54Z","cross_cats_sorted":["cs.PF"],"title_canon_sha256":"9fbe131b37d0bb5395bc27905c9e43863110cf5fe2c29d6a405dbf493e89659f","abstract_canon_sha256":"2bc152c6e93c551bec3bd7922471176e4feedce7dd50d6dab5d33b9f287313d1"},"schema_version":"1.0"},"canonical_sha256":"6b9ce01922fff7f2c6be9e4b99cb938306a386ed9cf621ceca94f7746d4d91c6","source":{"kind":"arxiv","id":"1911.04610","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.04610","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1911.04610v3","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.04610","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"NOOOAGJC7737","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"pith_short_16","alias_value":"NOOOAGJC7737FRV6","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"pith_short_8","alias_value":"NOOOAGJC","created_at":"2026-07-05T01:49:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NOOOAGJC7737FRV6TZFZTS4TQM","target":"record","payload":{"canonical_record":{"source":{"id":"1911.04610","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-24T00:13:54Z","cross_cats_sorted":["cs.PF"],"title_canon_sha256":"9fbe131b37d0bb5395bc27905c9e43863110cf5fe2c29d6a405dbf493e89659f","abstract_canon_sha256":"2bc152c6e93c551bec3bd7922471176e4feedce7dd50d6dab5d33b9f287313d1"},"schema_version":"1.0"},"canonical_sha256":"6b9ce01922fff7f2c6be9e4b99cb938306a386ed9cf621ceca94f7746d4d91c6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:49:48.339382Z","signature_b64":"21OIbmQ3LXkU94/c6KWNnrZE5xkcZNqSgVUSVpEWSX82dIhQ5fa7FINkcXoGyXsCNhJWByNW2GDtV6gDN5f/AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b9ce01922fff7f2c6be9e4b99cb938306a386ed9cf621ceca94f7746d4d91c6","last_reissued_at":"2026-07-05T01:49:48.338901Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:49:48.338901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.04610","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uRk2jCLobffDk2s3uRcw2h7QEd0xNyVTUVYEBiD4gjHeJ8np/RyDDoXxsNef6LUUGzQQH6njVnyCgVefzbepBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T08:47:33.089654Z"},"content_sha256":"c065e474a7372d92e47583e88959265182f1b22bfe1732c44a23098a50752f47","schema_version":"1.0","event_id":"sha256:c065e474a7372d92e47583e88959265182f1b22bfe1732c44a23098a50752f47"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NOOOAGJC7737FRV6TZFZTS4TQM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.LG","authors_text":"Dongsheng Li, Lei Guan, Wotao Yin, Xicheng Lu","submitted_at":"2019-10-24T00:13:54Z","abstract_excerpt":"We propose XPipe, an efficient asynchronous pipeline model parallelism approach for multi-GPU DNN training. XPipe is designed to use multiple GPUs to concurrently and continuously train different parts of a DNN model. To improve GPU utilization and achieve high throughput, it splits a mini-batch into a set of micro-batches. It allows the overlapping of the pipelines of multiple micro-batches, including those belonging to different mini-batches. Most importantly, the novel weight prediction strategy adopted by XPipe enables it to effectively address the weight inconsistency and staleness issues"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.04610","kind":"arxiv","version":3},"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/1911.04610/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":"2026-07-05T01:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vnNC4jMNwqh6QtsSXqGUoma/bwTPfbGFeKBIb1b8dXFqwbTKzfxaJj5ce1yCQXx4qy+6OhcfaZtcnF9pNabWDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T08:47:33.090171Z"},"content_sha256":"228b30f2c5caa704060436166eb5a174d1451b6d25140b7588de8ddf5bcfbd1e","schema_version":"1.0","event_id":"sha256:228b30f2c5caa704060436166eb5a174d1451b6d25140b7588de8ddf5bcfbd1e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NOOOAGJC7737FRV6TZFZTS4TQM/bundle.json","state_url":"https://pith.science/pith/NOOOAGJC7737FRV6TZFZTS4TQM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NOOOAGJC7737FRV6TZFZTS4TQM/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":"2026-08-18T08:47:33Z","links":{"resolver":"https://pith.science/pith/NOOOAGJC7737FRV6TZFZTS4TQM","bundle":"https://pith.science/pith/NOOOAGJC7737FRV6TZFZTS4TQM/bundle.json","state":"https://pith.science/pith/NOOOAGJC7737FRV6TZFZTS4TQM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NOOOAGJC7737FRV6TZFZTS4TQM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NOOOAGJC7737FRV6TZFZTS4TQM","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":"2bc152c6e93c551bec3bd7922471176e4feedce7dd50d6dab5d33b9f287313d1","cross_cats_sorted":["cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-24T00:13:54Z","title_canon_sha256":"9fbe131b37d0bb5395bc27905c9e43863110cf5fe2c29d6a405dbf493e89659f"},"schema_version":"1.0","source":{"id":"1911.04610","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.04610","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1911.04610v3","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.04610","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"NOOOAGJC7737","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"pith_short_16","alias_value":"NOOOAGJC7737FRV6","created_at":"2026-07-05T01:49:48Z"},{"alias_kind":"pith_short_8","alias_value":"NOOOAGJC","created_at":"2026-07-05T01:49:48Z"}],"graph_snapshots":[{"event_id":"sha256:228b30f2c5caa704060436166eb5a174d1451b6d25140b7588de8ddf5bcfbd1e","target":"graph","created_at":"2026-07-05T01:49:48Z","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/1911.04610/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose XPipe, an efficient asynchronous pipeline model parallelism approach for multi-GPU DNN training. XPipe is designed to use multiple GPUs to concurrently and continuously train different parts of a DNN model. To improve GPU utilization and achieve high throughput, it splits a mini-batch into a set of micro-batches. It allows the overlapping of the pipelines of multiple micro-batches, including those belonging to different mini-batches. Most importantly, the novel weight prediction strategy adopted by XPipe enables it to effectively address the weight inconsistency and staleness issues","authors_text":"Dongsheng Li, Lei Guan, Wotao Yin, Xicheng Lu","cross_cats":["cs.PF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-24T00:13:54Z","title":"XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.04610","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:c065e474a7372d92e47583e88959265182f1b22bfe1732c44a23098a50752f47","target":"record","created_at":"2026-07-05T01:49:48Z","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":"2bc152c6e93c551bec3bd7922471176e4feedce7dd50d6dab5d33b9f287313d1","cross_cats_sorted":["cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-24T00:13:54Z","title_canon_sha256":"9fbe131b37d0bb5395bc27905c9e43863110cf5fe2c29d6a405dbf493e89659f"},"schema_version":"1.0","source":{"id":"1911.04610","kind":"arxiv","version":3}},"canonical_sha256":"6b9ce01922fff7f2c6be9e4b99cb938306a386ed9cf621ceca94f7746d4d91c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6b9ce01922fff7f2c6be9e4b99cb938306a386ed9cf621ceca94f7746d4d91c6","first_computed_at":"2026-07-05T01:49:48.338901Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:48.338901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"21OIbmQ3LXkU94/c6KWNnrZE5xkcZNqSgVUSVpEWSX82dIhQ5fa7FINkcXoGyXsCNhJWByNW2GDtV6gDN5f/AA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:48.339382Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.04610","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c065e474a7372d92e47583e88959265182f1b22bfe1732c44a23098a50752f47","sha256:228b30f2c5caa704060436166eb5a174d1451b6d25140b7588de8ddf5bcfbd1e"],"state_sha256":"bd79e00ba43cd72d5b3019809be6db7111088a6fd52d846daeba991ac79872d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+PO6YBSWtsuACED6V1QdpK0OhKmp/uRX2/gnZBXKFN0fv4KcwTTWBJd9hjn5X1WBW4JuXHERk2iVPCf6ikkvBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T08:47:33.095176Z","bundle_sha256":"98e903fba449c57748aeb1274794b34f11ca7ebad7d3feb6023488faa85556bd"}}