{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:UYPVJX2HLVCUA5M3Y3ZYVP4L46","short_pith_number":"pith:UYPVJX2H","canonical_record":{"source":{"id":"2006.09503","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-16T20:33:54Z","cross_cats_sorted":["cs.DC","stat.ML"],"title_canon_sha256":"d6e59f45561a0fe8af913ac8c27c7034ce4c0dc781f9a4d5949ebf589d6db0c0","abstract_canon_sha256":"e386ffdb742082225684495496b00499ae6248ef94b4782fc2f2a07a8d0a0c8a"},"schema_version":"1.0"},"canonical_sha256":"a61f54df475d4540759bc6f38abf8be78052dfdbc50273eb2b58994fe58ec0ff","source":{"kind":"arxiv","id":"2006.09503","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.09503","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"arxiv_version","alias_value":"2006.09503v3","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09503","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"pith_short_12","alias_value":"UYPVJX2HLVCU","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"pith_short_16","alias_value":"UYPVJX2HLVCUA5M3","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"pith_short_8","alias_value":"UYPVJX2H","created_at":"2026-07-05T02:59:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:UYPVJX2HLVCUA5M3Y3ZYVP4L46","target":"record","payload":{"canonical_record":{"source":{"id":"2006.09503","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-16T20:33:54Z","cross_cats_sorted":["cs.DC","stat.ML"],"title_canon_sha256":"d6e59f45561a0fe8af913ac8c27c7034ce4c0dc781f9a4d5949ebf589d6db0c0","abstract_canon_sha256":"e386ffdb742082225684495496b00499ae6248ef94b4782fc2f2a07a8d0a0c8a"},"schema_version":"1.0"},"canonical_sha256":"a61f54df475d4540759bc6f38abf8be78052dfdbc50273eb2b58994fe58ec0ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:59:52.898689Z","signature_b64":"JmzXbGzpcomvkaaLqj6fCUaHoPmqxPCFzSs7hexhGTVc4LhFPNxBkMlfLhgRd/BYGv4ASYs/1GHg6ssX/3M5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a61f54df475d4540759bc6f38abf8be78052dfdbc50273eb2b58994fe58ec0ff","last_reissued_at":"2026-07-05T02:59:52.898337Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:59:52.898337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.09503","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":"2026-07-05T02:59:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ha0gyGuGtdWjZ9GexD0CCLCKcjFPYIRlenlVVhXPKC0EOPX4YcSB6nuD5zjCoA7rY5ey6QCkFt5OXyUQ4jUEDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:54:02.327516Z"},"content_sha256":"c53eb6dd6f05d3bb1881a1cc877134ee03c51c4f3ba46d7f62a0b795e3433552","schema_version":"1.0","event_id":"sha256:c53eb6dd6f05d3bb1881a1cc877134ee03c51c4f3ba46d7f62a0b795e3433552"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:UYPVJX2HLVCUA5M3Y3ZYVP4L46","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Memory-Efficient Pipeline-Parallel DNN Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Amar Phanishayee, Deepak Narayanan, Kaiyu Shi, Matei Zaharia, Xie Chen","submitted_at":"2020-06-16T20:33:54Z","abstract_excerpt":"Many state-of-the-art ML results have been obtained by scaling up the number of parameters in existing models. However, parameters and activations for such large models often do not fit in the memory of a single accelerator device; this means that it is necessary to distribute training of large models over multiple accelerators. In this work, we propose PipeDream-2BW, a system that supports memory-efficient pipeline parallelism. PipeDream-2BW uses a novel pipelining and weight gradient coalescing strategy, combined with the double buffering of weights, to ensure high throughput, low memory foo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09503","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/2006.09503/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":"2026-07-05T02:59:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sV7KR+svp4Ehc2TqPNxLjYffAIT6PHWJaQBLwvK5lqNx9wq2GgLrLdWM7osqE7VHqdKbx7nPQ31vo7fA0wRdDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:54:02.328077Z"},"content_sha256":"851b8dfb403d630b09ef917cda375b6f97f53c31e9bee6d32ed7017757387eda","schema_version":"1.0","event_id":"sha256:851b8dfb403d630b09ef917cda375b6f97f53c31e9bee6d32ed7017757387eda"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UYPVJX2HLVCUA5M3Y3ZYVP4L46/bundle.json","state_url":"https://pith.science/pith/UYPVJX2HLVCUA5M3Y3ZYVP4L46/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UYPVJX2HLVCUA5M3Y3ZYVP4L46/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":"2026-08-04T14:54:02Z","links":{"resolver":"https://pith.science/pith/UYPVJX2HLVCUA5M3Y3ZYVP4L46","bundle":"https://pith.science/pith/UYPVJX2HLVCUA5M3Y3ZYVP4L46/bundle.json","state":"https://pith.science/pith/UYPVJX2HLVCUA5M3Y3ZYVP4L46/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UYPVJX2HLVCUA5M3Y3ZYVP4L46/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:UYPVJX2HLVCUA5M3Y3ZYVP4L46","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":"e386ffdb742082225684495496b00499ae6248ef94b4782fc2f2a07a8d0a0c8a","cross_cats_sorted":["cs.DC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-16T20:33:54Z","title_canon_sha256":"d6e59f45561a0fe8af913ac8c27c7034ce4c0dc781f9a4d5949ebf589d6db0c0"},"schema_version":"1.0","source":{"id":"2006.09503","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.09503","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"arxiv_version","alias_value":"2006.09503v3","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09503","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"pith_short_12","alias_value":"UYPVJX2HLVCU","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"pith_short_16","alias_value":"UYPVJX2HLVCUA5M3","created_at":"2026-07-05T02:59:52Z"},{"alias_kind":"pith_short_8","alias_value":"UYPVJX2H","created_at":"2026-07-05T02:59:52Z"}],"graph_snapshots":[{"event_id":"sha256:851b8dfb403d630b09ef917cda375b6f97f53c31e9bee6d32ed7017757387eda","target":"graph","created_at":"2026-07-05T02:59:52Z","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/2006.09503/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many state-of-the-art ML results have been obtained by scaling up the number of parameters in existing models. However, parameters and activations for such large models often do not fit in the memory of a single accelerator device; this means that it is necessary to distribute training of large models over multiple accelerators. In this work, we propose PipeDream-2BW, a system that supports memory-efficient pipeline parallelism. PipeDream-2BW uses a novel pipelining and weight gradient coalescing strategy, combined with the double buffering of weights, to ensure high throughput, low memory foo","authors_text":"Amar Phanishayee, Deepak Narayanan, Kaiyu Shi, Matei Zaharia, Xie Chen","cross_cats":["cs.DC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-16T20:33:54Z","title":"Memory-Efficient Pipeline-Parallel DNN Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09503","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:c53eb6dd6f05d3bb1881a1cc877134ee03c51c4f3ba46d7f62a0b795e3433552","target":"record","created_at":"2026-07-05T02:59:52Z","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":"e386ffdb742082225684495496b00499ae6248ef94b4782fc2f2a07a8d0a0c8a","cross_cats_sorted":["cs.DC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-16T20:33:54Z","title_canon_sha256":"d6e59f45561a0fe8af913ac8c27c7034ce4c0dc781f9a4d5949ebf589d6db0c0"},"schema_version":"1.0","source":{"id":"2006.09503","kind":"arxiv","version":3}},"canonical_sha256":"a61f54df475d4540759bc6f38abf8be78052dfdbc50273eb2b58994fe58ec0ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a61f54df475d4540759bc6f38abf8be78052dfdbc50273eb2b58994fe58ec0ff","first_computed_at":"2026-07-05T02:59:52.898337Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:59:52.898337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JmzXbGzpcomvkaaLqj6fCUaHoPmqxPCFzSs7hexhGTVc4LhFPNxBkMlfLhgRd/BYGv4ASYs/1GHg6ssX/3M5CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:59:52.898689Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.09503","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c53eb6dd6f05d3bb1881a1cc877134ee03c51c4f3ba46d7f62a0b795e3433552","sha256:851b8dfb403d630b09ef917cda375b6f97f53c31e9bee6d32ed7017757387eda"],"state_sha256":"3b0dfebac2273085088cdbc34e17f3616a856affd8206bc3b7b1d15de50d6bcd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T0tEjIpkB0DXd2e30vvU1KTksKiuj3BwFCbPCgYuVpB2Ss3ap7DKMohazQHGEu73e2KCICCKOC6ugxXriTNPDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T14:54:02.334114Z","bundle_sha256":"978a29909f1a6a99a0a988aed5eb422062a359aba1439b72da9b7015370a9d81"}}