{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:EZWKOKT6QNWSQRYPN4CG2IGREK","short_pith_number":"pith:EZWKOKT6","schema_version":"1.0","canonical_sha256":"266ca72a7e836d28470f6f046d20d122b4cc6050f4c6cbd42783c685dece3f25","source":{"kind":"arxiv","id":"2201.01556","version":1},"attestation_state":"computed","paper":{"title":"Parallel Flow-Based Hypergraph Partitioning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.DS","authors_text":"Lars Gottesb\\\"uren, Peter Sanders, Tobias Heuer","submitted_at":"2022-01-05T11:57:16Z","abstract_excerpt":"We present a shared-memory parallelization of flow-based refinement, which is considered the most powerful iterative improvement technique for hypergraph partitioning at the moment. Flow-based refinement works on bipartitions, so current sequential partitioners schedule it on different block pairs to improve $k$-way partitions. We investigate two different sources of parallelism: a parallel scheduling scheme and a parallel maximum flow algorithm based on the well-known push-relabel algorithm. In addition to thoroughly engineered implementations, we propose several optimizations that substantia"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2201.01556","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DS","submitted_at":"2022-01-05T11:57:16Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"e2e8fd562a09dbd1c39276c36e0081c22f0b5250fc62f66ea492448a1ef5779f","abstract_canon_sha256":"d7caeb3e2dd5154400987d75f4a0a96efb34fdf56856dc45019283554c88e2dc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:46:06.750203Z","signature_b64":"s2XWHY8+AzEiPAXxFhFmFP+0jd0X+FJbJ11t/JuamAocXCDAfPrmeTin0yTKyY7xJEQkqUo4iwh5ACEZL34ZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"266ca72a7e836d28470f6f046d20d122b4cc6050f4c6cbd42783c685dece3f25","last_reissued_at":"2026-07-05T03:46:06.749701Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:46:06.749701Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parallel Flow-Based Hypergraph Partitioning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.DS","authors_text":"Lars Gottesb\\\"uren, Peter Sanders, Tobias Heuer","submitted_at":"2022-01-05T11:57:16Z","abstract_excerpt":"We present a shared-memory parallelization of flow-based refinement, which is considered the most powerful iterative improvement technique for hypergraph partitioning at the moment. Flow-based refinement works on bipartitions, so current sequential partitioners schedule it on different block pairs to improve $k$-way partitions. We investigate two different sources of parallelism: a parallel scheduling scheme and a parallel maximum flow algorithm based on the well-known push-relabel algorithm. In addition to thoroughly engineered implementations, we propose several optimizations that substantia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.01556","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/2201.01556/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2201.01556","created_at":"2026-07-05T03:46:06.749768+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.01556v1","created_at":"2026-07-05T03:46:06.749768+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.01556","created_at":"2026-07-05T03:46:06.749768+00:00"},{"alias_kind":"pith_short_12","alias_value":"EZWKOKT6QNWS","created_at":"2026-07-05T03:46:06.749768+00:00"},{"alias_kind":"pith_short_16","alias_value":"EZWKOKT6QNWSQRYP","created_at":"2026-07-05T03:46:06.749768+00:00"},{"alias_kind":"pith_short_8","alias_value":"EZWKOKT6","created_at":"2026-07-05T03:46:06.749768+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18117","citing_title":"IMPart: Integration of Memetic Operations into Multi-Level Framework for Large-k-Way Hypergraph Partitioning","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK","json":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK.json","graph_json":"https://pith.science/api/pith-number/EZWKOKT6QNWSQRYPN4CG2IGREK/graph.json","events_json":"https://pith.science/api/pith-number/EZWKOKT6QNWSQRYPN4CG2IGREK/events.json","paper":"https://pith.science/paper/EZWKOKT6"},"agent_actions":{"view_html":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK","download_json":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK.json","view_paper":"https://pith.science/paper/EZWKOKT6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.01556&json=true","fetch_graph":"https://pith.science/api/pith-number/EZWKOKT6QNWSQRYPN4CG2IGREK/graph.json","fetch_events":"https://pith.science/api/pith-number/EZWKOKT6QNWSQRYPN4CG2IGREK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK/action/storage_attestation","attest_author":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK/action/author_attestation","sign_citation":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK/action/citation_signature","submit_replication":"https://pith.science/pith/EZWKOKT6QNWSQRYPN4CG2IGREK/action/replication_record"}},"created_at":"2026-07-05T03:46:06.749768+00:00","updated_at":"2026-07-05T03:46:06.749768+00:00"}