{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WYSVJ7NKUMSAD7XFSH3U5TRPT3","short_pith_number":"pith:WYSVJ7NK","schema_version":"1.0","canonical_sha256":"b62554fdaaa32401fee591f74ece2f9ecfa0d2bd971242e65befbbffbe3b107c","source":{"kind":"arxiv","id":"2205.13603","version":2},"attestation_state":"computed","paper":{"title":"Tensor Program Optimization with Probabilistic Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bohan Hou, Cody Hao Yu, Hongyi Jin, Junru Shao, Masahiro Masuda, Ruihang Lai, Siyuan Feng, Tianqi Chen, Wuwei Lin, Xiyou Zhou","submitted_at":"2022-05-26T20:20:02Z","abstract_excerpt":"Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space which lacks the ability to efficiently enable domain experts to grow the search space. This paper introduces MetaSchedule, a domain-specific probabilistic programming language abstraction to construct a rich search space of tensor programs. Our abstraction allows domain experts to analyze the program, and easily propose stochastic choices in a modula"},"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":"2205.13603","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-26T20:20:02Z","cross_cats_sorted":[],"title_canon_sha256":"3dabc5ef34605f8b94340dda5e46e9a568e11c9514240ee3ff7b56cdbd2ffdf8","abstract_canon_sha256":"42903b10a3a8f824b7f0e1bd6906f4b102a58bd97e70916bd7123d4d6b9886ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:04:48.932929Z","signature_b64":"ckCbbjDn1Is+CVedFWxdbu14VUbJeTIKmt7aHrtx/QviZInQISSJgUhfisyM010yn8ZmdwngoXUOFl5TiO+qDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b62554fdaaa32401fee591f74ece2f9ecfa0d2bd971242e65befbbffbe3b107c","last_reissued_at":"2026-07-05T05:04:48.932398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:04:48.932398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tensor Program Optimization with Probabilistic Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bohan Hou, Cody Hao Yu, Hongyi Jin, Junru Shao, Masahiro Masuda, Ruihang Lai, Siyuan Feng, Tianqi Chen, Wuwei Lin, Xiyou Zhou","submitted_at":"2022-05-26T20:20:02Z","abstract_excerpt":"Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space which lacks the ability to efficiently enable domain experts to grow the search space. This paper introduces MetaSchedule, a domain-specific probabilistic programming language abstraction to construct a rich search space of tensor programs. Our abstraction allows domain experts to analyze the program, and easily propose stochastic choices in a modula"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.13603","kind":"arxiv","version":2},"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/2205.13603/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":"2205.13603","created_at":"2026-07-05T05:04:48.932458+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.13603v2","created_at":"2026-07-05T05:04:48.932458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.13603","created_at":"2026-07-05T05:04:48.932458+00:00"},{"alias_kind":"pith_short_12","alias_value":"WYSVJ7NKUMSA","created_at":"2026-07-05T05:04:48.932458+00:00"},{"alias_kind":"pith_short_16","alias_value":"WYSVJ7NKUMSAD7XF","created_at":"2026-07-05T05:04:48.932458+00:00"},{"alias_kind":"pith_short_8","alias_value":"WYSVJ7NK","created_at":"2026-07-05T05:04:48.932458+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.11109","citing_title":"Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3","json":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3.json","graph_json":"https://pith.science/api/pith-number/WYSVJ7NKUMSAD7XFSH3U5TRPT3/graph.json","events_json":"https://pith.science/api/pith-number/WYSVJ7NKUMSAD7XFSH3U5TRPT3/events.json","paper":"https://pith.science/paper/WYSVJ7NK"},"agent_actions":{"view_html":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3","download_json":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3.json","view_paper":"https://pith.science/paper/WYSVJ7NK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.13603&json=true","fetch_graph":"https://pith.science/api/pith-number/WYSVJ7NKUMSAD7XFSH3U5TRPT3/graph.json","fetch_events":"https://pith.science/api/pith-number/WYSVJ7NKUMSAD7XFSH3U5TRPT3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3/action/storage_attestation","attest_author":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3/action/author_attestation","sign_citation":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3/action/citation_signature","submit_replication":"https://pith.science/pith/WYSVJ7NKUMSAD7XFSH3U5TRPT3/action/replication_record"}},"created_at":"2026-07-05T05:04:48.932458+00:00","updated_at":"2026-07-05T05:04:48.932458+00:00"}