{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:PXGTXQ3HJRB5IVQWERDNPXPT4V","short_pith_number":"pith:PXGTXQ3H","schema_version":"1.0","canonical_sha256":"7dcd3bc3674c43d456162446d7ddf3e57e8f01182c68e9721ff86d56cf35aa14","source":{"kind":"arxiv","id":"1704.02432","version":3},"attestation_state":"computed","paper":{"title":"Dynamic Race Prediction in Linear Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.PL","authors_text":"Dileep Kini, Mahesh Viswanathan, Umang Mathur","submitted_at":"2017-04-08T03:14:13Z","abstract_excerpt":"Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causally-precedes (CP) and maximal causal models fall short due to the fact that the"},"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":"1704.02432","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2017-04-08T03:14:13Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"3f5c64fa833cfed8688a8dbeb13c60987e586b4bcfcaf8fc60fb2304641c7ed4","abstract_canon_sha256":"5c85fe41389bcd108d570fd2b1f90453f6aab6ccbc16b6c304e1e4de0a58c48d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:46.864847Z","signature_b64":"UMR3kccLCqUxJYzBrgzz7fRLqr7wUQh4oone5LaePB0LSfWthvgFb1zVGmq0XEnWlOI1VeoGEPAt2mKJCf1VBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7dcd3bc3674c43d456162446d7ddf3e57e8f01182c68e9721ff86d56cf35aa14","last_reissued_at":"2026-05-18T00:27:46.864358Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:46.864358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Race Prediction in Linear Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.PL","authors_text":"Dileep Kini, Mahesh Viswanathan, Umang Mathur","submitted_at":"2017-04-08T03:14:13Z","abstract_excerpt":"Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causally-precedes (CP) and maximal causal models fall short due to the fact that the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02432","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":""},"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":"1704.02432","created_at":"2026-05-18T00:27:46.864434+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.02432v3","created_at":"2026-05-18T00:27:46.864434+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02432","created_at":"2026-05-18T00:27:46.864434+00:00"},{"alias_kind":"pith_short_12","alias_value":"PXGTXQ3HJRB5","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"PXGTXQ3HJRB5IVQW","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"PXGTXQ3H","created_at":"2026-05-18T12:31:37.085036+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2502.20070","citing_title":"Partial Orders for Precise and Efficient Dynamic Deadlock Prediction","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2603.13142","citing_title":"Critical Sections Are Not Per-Thread: A Trace Semantics for Lock-Based Concurrency","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V","json":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V.json","graph_json":"https://pith.science/api/pith-number/PXGTXQ3HJRB5IVQWERDNPXPT4V/graph.json","events_json":"https://pith.science/api/pith-number/PXGTXQ3HJRB5IVQWERDNPXPT4V/events.json","paper":"https://pith.science/paper/PXGTXQ3H"},"agent_actions":{"view_html":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V","download_json":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V.json","view_paper":"https://pith.science/paper/PXGTXQ3H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.02432&json=true","fetch_graph":"https://pith.science/api/pith-number/PXGTXQ3HJRB5IVQWERDNPXPT4V/graph.json","fetch_events":"https://pith.science/api/pith-number/PXGTXQ3HJRB5IVQWERDNPXPT4V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V/action/storage_attestation","attest_author":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V/action/author_attestation","sign_citation":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V/action/citation_signature","submit_replication":"https://pith.science/pith/PXGTXQ3HJRB5IVQWERDNPXPT4V/action/replication_record"}},"created_at":"2026-05-18T00:27:46.864434+00:00","updated_at":"2026-05-18T00:27:46.864434+00:00"}