{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:P3PYN7PX3S7INYJSJDV72YUTY4","short_pith_number":"pith:P3PYN7PX","schema_version":"1.0","canonical_sha256":"7edf86fdf7dcbe86e13248ebfd6293c7211e96594de70d7a14a5e0cc025ff009","source":{"kind":"arxiv","id":"1211.0557","version":1},"attestation_state":"computed","paper":{"title":"Stochastic Superoptimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PL"],"primary_cat":"cs.PF","authors_text":"Alex Aiken, Eric Schkufza, Rahul Sharma","submitted_at":"2012-11-02T20:23:23Z","abstract_excerpt":"We formulate the loop-free, binary superoptimization task as a stochastic search problem. The competing constraints of transformation correctness and performance improvement are encoded as terms in a cost function, and a Markov Chain Monte Carlo sampler is used to rapidly explore the space of all possible programs to find one that is an optimization of a given target program. Although our method sacrifices com- pleteness, the scope of programs we are able to reason about, and the quality of the programs we produce, far exceed those of existing superoptimizers. Beginning from binaries com- pile"},"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":"1211.0557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2012-11-02T20:23:23Z","cross_cats_sorted":["cs.PL"],"title_canon_sha256":"39a3e72f68801fc58dc7af6606fa38420cb350b43e67cddc6261e77f076bdce2","abstract_canon_sha256":"5fa4c2484585047063a5d602cb6442ab8c3dea8fa708e547000764d67a5796cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:41:33.105897Z","signature_b64":"4wQVX4k4BqNvkcbtyzbYbsd/Dyvl2/mO8mgs2/i+KZIG9Lzo3CLQIS0RHC+0vEfGEZc/FRbx4zDEuO9TlfvyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7edf86fdf7dcbe86e13248ebfd6293c7211e96594de70d7a14a5e0cc025ff009","last_reissued_at":"2026-05-18T03:41:33.105350Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:41:33.105350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stochastic Superoptimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PL"],"primary_cat":"cs.PF","authors_text":"Alex Aiken, Eric Schkufza, Rahul Sharma","submitted_at":"2012-11-02T20:23:23Z","abstract_excerpt":"We formulate the loop-free, binary superoptimization task as a stochastic search problem. The competing constraints of transformation correctness and performance improvement are encoded as terms in a cost function, and a Markov Chain Monte Carlo sampler is used to rapidly explore the space of all possible programs to find one that is an optimization of a given target program. Although our method sacrifices com- pleteness, the scope of programs we are able to reason about, and the quality of the programs we produce, far exceed those of existing superoptimizers. Beginning from binaries com- pile"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.0557","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":""},"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":"1211.0557","created_at":"2026-05-18T03:41:33.105426+00:00"},{"alias_kind":"arxiv_version","alias_value":"1211.0557v1","created_at":"2026-05-18T03:41:33.105426+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.0557","created_at":"2026-05-18T03:41:33.105426+00:00"},{"alias_kind":"pith_short_12","alias_value":"P3PYN7PX3S7I","created_at":"2026-05-18T12:27:18.751474+00:00"},{"alias_kind":"pith_short_16","alias_value":"P3PYN7PX3S7INYJS","created_at":"2026-05-18T12:27:18.751474+00:00"},{"alias_kind":"pith_short_8","alias_value":"P3PYN7PX","created_at":"2026-05-18T12:27:18.751474+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4","json":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4.json","graph_json":"https://pith.science/api/pith-number/P3PYN7PX3S7INYJSJDV72YUTY4/graph.json","events_json":"https://pith.science/api/pith-number/P3PYN7PX3S7INYJSJDV72YUTY4/events.json","paper":"https://pith.science/paper/P3PYN7PX"},"agent_actions":{"view_html":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4","download_json":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4.json","view_paper":"https://pith.science/paper/P3PYN7PX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1211.0557&json=true","fetch_graph":"https://pith.science/api/pith-number/P3PYN7PX3S7INYJSJDV72YUTY4/graph.json","fetch_events":"https://pith.science/api/pith-number/P3PYN7PX3S7INYJSJDV72YUTY4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4/action/storage_attestation","attest_author":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4/action/author_attestation","sign_citation":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4/action/citation_signature","submit_replication":"https://pith.science/pith/P3PYN7PX3S7INYJSJDV72YUTY4/action/replication_record"}},"created_at":"2026-05-18T03:41:33.105426+00:00","updated_at":"2026-05-18T03:41:33.105426+00:00"}