{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:A5MDAYORWPZ2BL7HGEBFZMFHRV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"22366d37ce641679050137e68775e9ed4a7fdcbf49cc19463bc4e64d9276c6f9","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:24:01Z","title_canon_sha256":"ef2286366cbe505e92bcbee0716910c5c198d662136f725812320eae5aeea8cc"},"schema_version":"1.0","source":{"id":"2310.16792","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.16792","created_at":"2026-07-05T08:58:20Z"},{"alias_kind":"arxiv_version","alias_value":"2310.16792v3","created_at":"2026-07-05T08:58:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.16792","created_at":"2026-07-05T08:58:20Z"},{"alias_kind":"pith_short_12","alias_value":"A5MDAYORWPZ2","created_at":"2026-07-05T08:58:20Z"},{"alias_kind":"pith_short_16","alias_value":"A5MDAYORWPZ2BL7H","created_at":"2026-07-05T08:58:20Z"},{"alias_kind":"pith_short_8","alias_value":"A5MDAYOR","created_at":"2026-07-05T08:58:20Z"}],"graph_snapshots":[{"event_id":"sha256:a319d56e46325e262510922c7d9fbbec79b7b4fc71b7fa4c731ab70c2fc6bfa4","target":"graph","created_at":"2026-07-05T08:58:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.16792/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Performance modeling is an essential tool in many areas, including performance characterization/optimization, design space exploration, and resource allocation problems, to name a few. However, existing performance modeling approaches have limitations, such as high computational cost for discrete-event simulators, narrow flexibility of hardware emulators, or restricted accuracy/generality of analytical/data-driven models. To address these limitations, this paper proposes PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional and independent/orthogonal ","authors_text":"Adolfy Hoisie, Lingda Li, Thomas Flynn","cross_cats":["cs.AR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:24:01Z","title":"Learning Generalizable Program and Architecture Representations for Performance Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.16792","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:60928881b6b457ccd5f0883f8c48c52491539c2e58ff055b4b5e078781751e75","target":"record","created_at":"2026-07-05T08:58:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"22366d37ce641679050137e68775e9ed4a7fdcbf49cc19463bc4e64d9276c6f9","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-25T17:24:01Z","title_canon_sha256":"ef2286366cbe505e92bcbee0716910c5c198d662136f725812320eae5aeea8cc"},"schema_version":"1.0","source":{"id":"2310.16792","kind":"arxiv","version":3}},"canonical_sha256":"07583061d1b3f3a0afe731025cb0a78d4bb7725bb59da3aa51a3a8d1b92ac47e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07583061d1b3f3a0afe731025cb0a78d4bb7725bb59da3aa51a3a8d1b92ac47e","first_computed_at":"2026-07-05T08:58:20.559657Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:58:20.559657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SNToKA+gk0I3OowB1qT+LTkB1MvLK7bDax6TsSstAzqgscAuQdm7JKfQqG/iDDhMdIW7P3HEJuHsTL/nICY+BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:58:20.560151Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.16792","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:60928881b6b457ccd5f0883f8c48c52491539c2e58ff055b4b5e078781751e75","sha256:a319d56e46325e262510922c7d9fbbec79b7b4fc71b7fa4c731ab70c2fc6bfa4"],"state_sha256":"08cef16326eded9badd9ec9024d06df0f1fc40bd5327791f3f30dcc58c03ea3b"}