{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:DM6TOMGONJQWKHCTJEDBGFJZFU","short_pith_number":"pith:DM6TOMGO","schema_version":"1.0","canonical_sha256":"1b3d3730ce6a61651c5349061315392d22befaf51fdd0875c7b3ea4c1c755ded","source":{"kind":"arxiv","id":"2010.12438","version":2},"attestation_state":"computed","paper":{"title":"Transferable Graph Optimizers for ML Compilers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"AmirAli Abdolrashidi, Anna Goldie, Azalia Mirhoseini, Daniel Wong, Hanxiao Liu, James Laudon, Peter Ma, Phitchaya Mangpo Phothilimthana, Qiumin Xu, Shen Wang, Sudip Roy, Yanqi Zhou","submitted_at":"2020-10-21T20:28:33Z","abstract_excerpt":"Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics based algorithms to solve these optimization problems one at a time. However, this approach is not only hard to maintain but often leads to sub-optimal solutions especially for newer model architectures. Existing learning based approaches in the literature are sample inefficient, tackle a single optimization problem, and do not generalize to unseen graphs making them infeasible to be deployed in practice. To address "},"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":"2010.12438","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-21T20:28:33Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"0b0d774c63c6eb56efedb3c3a49927591498354afeb00d94c1835e43be362fa6","abstract_canon_sha256":"3dfbebbf03c3833fafad15c14774e5c3629aca96da87eb6fb4221d1e7b60ca16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:16:50.160810Z","signature_b64":"5YupVGxKcEl0Pc56jTU92bwVXSP16Itv3tY7/b8oHpDgeb/G9geS3J19fgrogJ5hRDKuuszmAe0k0GcxlIy/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b3d3730ce6a61651c5349061315392d22befaf51fdd0875c7b3ea4c1c755ded","last_reissued_at":"2026-07-05T02:16:50.160417Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:16:50.160417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transferable Graph Optimizers for ML Compilers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"AmirAli Abdolrashidi, Anna Goldie, Azalia Mirhoseini, Daniel Wong, Hanxiao Liu, James Laudon, Peter Ma, Phitchaya Mangpo Phothilimthana, Qiumin Xu, Shen Wang, Sudip Roy, Yanqi Zhou","submitted_at":"2020-10-21T20:28:33Z","abstract_excerpt":"Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics based algorithms to solve these optimization problems one at a time. However, this approach is not only hard to maintain but often leads to sub-optimal solutions especially for newer model architectures. Existing learning based approaches in the literature are sample inefficient, tackle a single optimization problem, and do not generalize to unseen graphs making them infeasible to be deployed in practice. To address "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.12438","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/2010.12438/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":"2010.12438","created_at":"2026-07-05T02:16:50.160474+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.12438v2","created_at":"2026-07-05T02:16:50.160474+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.12438","created_at":"2026-07-05T02:16:50.160474+00:00"},{"alias_kind":"pith_short_12","alias_value":"DM6TOMGONJQW","created_at":"2026-07-05T02:16:50.160474+00:00"},{"alias_kind":"pith_short_16","alias_value":"DM6TOMGONJQWKHCT","created_at":"2026-07-05T02:16:50.160474+00:00"},{"alias_kind":"pith_short_8","alias_value":"DM6TOMGO","created_at":"2026-07-05T02:16:50.160474+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/DM6TOMGONJQWKHCTJEDBGFJZFU","json":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU.json","graph_json":"https://pith.science/api/pith-number/DM6TOMGONJQWKHCTJEDBGFJZFU/graph.json","events_json":"https://pith.science/api/pith-number/DM6TOMGONJQWKHCTJEDBGFJZFU/events.json","paper":"https://pith.science/paper/DM6TOMGO"},"agent_actions":{"view_html":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU","download_json":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU.json","view_paper":"https://pith.science/paper/DM6TOMGO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.12438&json=true","fetch_graph":"https://pith.science/api/pith-number/DM6TOMGONJQWKHCTJEDBGFJZFU/graph.json","fetch_events":"https://pith.science/api/pith-number/DM6TOMGONJQWKHCTJEDBGFJZFU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU/action/storage_attestation","attest_author":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU/action/author_attestation","sign_citation":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU/action/citation_signature","submit_replication":"https://pith.science/pith/DM6TOMGONJQWKHCTJEDBGFJZFU/action/replication_record"}},"created_at":"2026-07-05T02:16:50.160474+00:00","updated_at":"2026-07-05T02:16:50.160474+00:00"}