{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:TT7V7YPRUSGFMKOWPYTVKBRE74","short_pith_number":"pith:TT7V7YPR","canonical_record":{"source":{"id":"2211.00235","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2022-11-01T02:59:35Z","cross_cats_sorted":[],"title_canon_sha256":"0743614e0dd7a6c71d7185e0e06f63e64f4e4a1b8b33542bd25a980102cb7758","abstract_canon_sha256":"89d0d044f03c3cfbd65222f862f4bfee040ca2a7a021ed4fda38d16dc71d658a"},"schema_version":"1.0"},"canonical_sha256":"9cff5fe1f1a48c5629d67e27550624ff1a095cebae4d0b4820bc34f3b4d2810f","source":{"kind":"arxiv","id":"2211.00235","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.00235","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"arxiv_version","alias_value":"2211.00235v1","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00235","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"pith_short_12","alias_value":"TT7V7YPRUSGF","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"pith_short_16","alias_value":"TT7V7YPRUSGFMKOW","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"pith_short_8","alias_value":"TT7V7YPR","created_at":"2026-07-05T05:12:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:TT7V7YPRUSGFMKOWPYTVKBRE74","target":"record","payload":{"canonical_record":{"source":{"id":"2211.00235","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2022-11-01T02:59:35Z","cross_cats_sorted":[],"title_canon_sha256":"0743614e0dd7a6c71d7185e0e06f63e64f4e4a1b8b33542bd25a980102cb7758","abstract_canon_sha256":"89d0d044f03c3cfbd65222f862f4bfee040ca2a7a021ed4fda38d16dc71d658a"},"schema_version":"1.0"},"canonical_sha256":"9cff5fe1f1a48c5629d67e27550624ff1a095cebae4d0b4820bc34f3b4d2810f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:20.167656Z","signature_b64":"Obo9LPSeKp9jKOrpfDlwhn4PgTgqPutj0mr0/xUN2LBJ4hodKzJbaSOYtxE1Ls2b5XfrivWBtrOUPv0XN1vGBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9cff5fe1f1a48c5629d67e27550624ff1a095cebae4d0b4820bc34f3b4d2810f","last_reissued_at":"2026-07-05T05:12:20.167157Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:20.167157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.00235","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:12:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f9RZJ3DxENbs3xvpzuoME/kfGDqFyC15xEApGnja9PtoNAmovox6sLO24UAKIundGec9y7xn53KaQohlzjZxCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:20:58.037416Z"},"content_sha256":"7462e12a35b77e9db3d4533e2a8f2c56032bbc96816a3dc008446e716c579b09","schema_version":"1.0","event_id":"sha256:7462e12a35b77e9db3d4533e2a8f2c56032bbc96816a3dc008446e716c579b09"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:TT7V7YPRUSGFMKOWPYTVKBRE74","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Dianhai Yu, Guoxia Wang, Xiaomin Fang, Yanjun Ma, Yingfei Xiang, Yiqun Liu, Zhihua Wu","submitted_at":"2022-11-01T02:59:35Z","abstract_excerpt":"The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to train AlphaFold2 from scratch. Efficient AlphaFold2 training could accelerate the development of life science. In this paper, we propose a Parallel Evoformer and Branch Parallelism to speed up the training of AlphaFold2. We conduct sufficient experiments on UniFold implemented in PyTorch and HelixFold implemented in PaddlePa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00235","kind":"arxiv","version":1},"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/2211.00235/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":"2026-07-05T05:12:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"utvN8I5DQHgrX9VINPROXcgG5ZL2M0HudYkoIoIsVKKUBVgsp+FZfLnrc6ZEP9+RlNWCLFB2+T5c8VXZbOHJCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:20:58.037843Z"},"content_sha256":"26716707477c4735e1210f925e6ac51542d1bd7d36f3676da668778c13e6e35e","schema_version":"1.0","event_id":"sha256:26716707477c4735e1210f925e6ac51542d1bd7d36f3676da668778c13e6e35e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TT7V7YPRUSGFMKOWPYTVKBRE74/bundle.json","state_url":"https://pith.science/pith/TT7V7YPRUSGFMKOWPYTVKBRE74/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TT7V7YPRUSGFMKOWPYTVKBRE74/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":"2026-07-09T02:20:58Z","links":{"resolver":"https://pith.science/pith/TT7V7YPRUSGFMKOWPYTVKBRE74","bundle":"https://pith.science/pith/TT7V7YPRUSGFMKOWPYTVKBRE74/bundle.json","state":"https://pith.science/pith/TT7V7YPRUSGFMKOWPYTVKBRE74/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TT7V7YPRUSGFMKOWPYTVKBRE74/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:TT7V7YPRUSGFMKOWPYTVKBRE74","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":"89d0d044f03c3cfbd65222f862f4bfee040ca2a7a021ed4fda38d16dc71d658a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2022-11-01T02:59:35Z","title_canon_sha256":"0743614e0dd7a6c71d7185e0e06f63e64f4e4a1b8b33542bd25a980102cb7758"},"schema_version":"1.0","source":{"id":"2211.00235","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.00235","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"arxiv_version","alias_value":"2211.00235v1","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00235","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"pith_short_12","alias_value":"TT7V7YPRUSGF","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"pith_short_16","alias_value":"TT7V7YPRUSGFMKOW","created_at":"2026-07-05T05:12:20Z"},{"alias_kind":"pith_short_8","alias_value":"TT7V7YPR","created_at":"2026-07-05T05:12:20Z"}],"graph_snapshots":[{"event_id":"sha256:26716707477c4735e1210f925e6ac51542d1bd7d36f3676da668778c13e6e35e","target":"graph","created_at":"2026-07-05T05:12:20Z","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/2211.00235/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to train AlphaFold2 from scratch. Efficient AlphaFold2 training could accelerate the development of life science. In this paper, we propose a Parallel Evoformer and Branch Parallelism to speed up the training of AlphaFold2. We conduct sufficient experiments on UniFold implemented in PyTorch and HelixFold implemented in PaddlePa","authors_text":"Dianhai Yu, Guoxia Wang, Xiaomin Fang, Yanjun Ma, Yingfei Xiang, Yiqun Liu, Zhihua Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2022-11-01T02:59:35Z","title":"Efficient AlphaFold2 Training using Parallel Evoformer and Branch Parallelism"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00235","kind":"arxiv","version":1},"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":"sha256:7462e12a35b77e9db3d4533e2a8f2c56032bbc96816a3dc008446e716c579b09","target":"record","created_at":"2026-07-05T05:12:20Z","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":"89d0d044f03c3cfbd65222f862f4bfee040ca2a7a021ed4fda38d16dc71d658a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2022-11-01T02:59:35Z","title_canon_sha256":"0743614e0dd7a6c71d7185e0e06f63e64f4e4a1b8b33542bd25a980102cb7758"},"schema_version":"1.0","source":{"id":"2211.00235","kind":"arxiv","version":1}},"canonical_sha256":"9cff5fe1f1a48c5629d67e27550624ff1a095cebae4d0b4820bc34f3b4d2810f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9cff5fe1f1a48c5629d67e27550624ff1a095cebae4d0b4820bc34f3b4d2810f","first_computed_at":"2026-07-05T05:12:20.167157Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:20.167157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Obo9LPSeKp9jKOrpfDlwhn4PgTgqPutj0mr0/xUN2LBJ4hodKzJbaSOYtxE1Ls2b5XfrivWBtrOUPv0XN1vGBA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:20.167656Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.00235","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7462e12a35b77e9db3d4533e2a8f2c56032bbc96816a3dc008446e716c579b09","sha256:26716707477c4735e1210f925e6ac51542d1bd7d36f3676da668778c13e6e35e"],"state_sha256":"8c26b8ccd0d9660bc77c0b8103412add07b09799d3de1ee7ab41fbbbd5fd7abf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A54twwVAF0pwBNcGph9riHLDvMKMaOe6a89XZrYFf2K7Ia+3f6fNbRSVgQCWu1nbRky2L5cw3TnH0bMTi2B3Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T02:20:58.039979Z","bundle_sha256":"7c69a61dcc8549050f398b66beeea628d4b3f93d81d6214b92dcac8f54f2636c"}}