{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:PR2IZNNZ2HGZ3TDCP7ZFQOYBMY","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":"e8ebbe99936700863882181fc3d8c04e4c6aa0c97deec2abf92ad45c71e90f61","cross_cats_sorted":["cs.LG","cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2021-03-16T02:03:39Z","title_canon_sha256":"be2c9984a9e76e73254df018514ecd355a7f70f257a1c237b75097b33db7967b"},"schema_version":"1.0","source":{"id":"2103.08802","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.08802","created_at":"2026-07-05T08:39:49Z"},{"alias_kind":"arxiv_version","alias_value":"2103.08802v1","created_at":"2026-07-05T08:39:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.08802","created_at":"2026-07-05T08:39:49Z"},{"alias_kind":"pith_short_12","alias_value":"PR2IZNNZ2HGZ","created_at":"2026-07-05T08:39:49Z"},{"alias_kind":"pith_short_16","alias_value":"PR2IZNNZ2HGZ3TDC","created_at":"2026-07-05T08:39:49Z"},{"alias_kind":"pith_short_8","alias_value":"PR2IZNNZ","created_at":"2026-07-05T08:39:49Z"}],"graph_snapshots":[{"event_id":"sha256:6d0ed0bc99f743f54e60782082a7ae2d3c747b77b593ee1cb4e0fe114902e528","target":"graph","created_at":"2026-07-05T08:39:49Z","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/2103.08802/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As deep neural networks (DNNs) become deeper, the training time increases. In this perspective, multi-GPU parallel computing has become a key tool in accelerating the training of DNNs. In this paper, we introduce a novel methodology to construct a parallel neural network that can utilize multiple GPUs simultaneously from a given DNN. We observe that layers of DNN can be interpreted as the time step of a time-dependent problem and can be parallelized by emulating a parallel-in-time algorithm called parareal. The parareal algorithm consists of fine structures which can be implemented in parallel","authors_text":"Chang-Ock Lee, Jongho Park, Youngkyu Lee","cross_cats":["cs.LG","cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2021-03-16T02:03:39Z","title":"Parareal Neural Networks Emulating a Parallel-in-time Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.08802","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":"sha256:791b7a589b839e22faf7469d1193e1a5ae44d7db8fb56ba122be3303c792c366","target":"record","created_at":"2026-07-05T08:39:49Z","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":"e8ebbe99936700863882181fc3d8c04e4c6aa0c97deec2abf92ad45c71e90f61","cross_cats_sorted":["cs.LG","cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2021-03-16T02:03:39Z","title_canon_sha256":"be2c9984a9e76e73254df018514ecd355a7f70f257a1c237b75097b33db7967b"},"schema_version":"1.0","source":{"id":"2103.08802","kind":"arxiv","version":1}},"canonical_sha256":"7c748cb5b9d1cd9dcc627ff2583b01660690fd837eeddb37cbe4615dbc936b4e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c748cb5b9d1cd9dcc627ff2583b01660690fd837eeddb37cbe4615dbc936b4e","first_computed_at":"2026-07-05T08:39:49.389478Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:39:49.389478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Jx4gMQETXiiR2UbqXC1lxlOdspskHgq65es8kKKsEseMV3PgrNwWVZ5s5tm7TC0U+2JO7o05c2g21btOQ0saBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:39:49.389917Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.08802","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:791b7a589b839e22faf7469d1193e1a5ae44d7db8fb56ba122be3303c792c366","sha256:6d0ed0bc99f743f54e60782082a7ae2d3c747b77b593ee1cb4e0fe114902e528"],"state_sha256":"4d3a6677386802859ad2247bf354444ae69b888bb14d1100acadd8d40ca74ce3"}