{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:7B2AJS6DXQYPKSCI27SNWI4FVL","short_pith_number":"pith:7B2AJS6D","schema_version":"1.0","canonical_sha256":"f87404cbc3bc30f54848d7e4db2385aae0e88ffedfd55a2aa899752c28aec1a0","source":{"kind":"arxiv","id":"2003.11740","version":1},"attestation_state":"computed","paper":{"title":"An Online Learning Methodology for Performance Modeling of Graphics Processors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SP"],"primary_cat":"eess.SY","authors_text":"Francesco Paterna, Manoj Babu, Michael Kishinevsky, Raid Ayoub, Suat Gumussoy, Ujjwal Gupta, Umit Ogras","submitted_at":"2020-03-26T04:32:02Z","abstract_excerpt":"Approximately 18 percent of the 3.2 million smartphone applications rely on integrated graphics processing units (GPUs) to achieve competitive performance. Graphics performance, typically measured in frames per second, is a strong function of the GPU frequency, which in turn has a significant impact on mobile processor power consumption. Consequently, dynamic power management algorithms have to assess the performance sensitivity to the frequency accurately to choose the operating frequency of the GPU effectively. Since the impact of GPU frequency on performance varies rapidly over time, there "},"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":"2003.11740","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2020-03-26T04:32:02Z","cross_cats_sorted":["cs.SY","eess.SP"],"title_canon_sha256":"4df2c71d08ed3682420820847b7693728f05536b94e6637fb19d394605e23196","abstract_canon_sha256":"afd9440ec4b7afecfb5212373d0cd73d721ff7a43479528a84a3ca0502e4e31d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:09:36.523867Z","signature_b64":"IJR2ygUZknCa7wvMiDjgbMg4OcAzjj2aXY9H8a1sDwYOOB6EWqkB/FMOiTqn020JY2I/TmjO9oF6bUyNwr5lAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f87404cbc3bc30f54848d7e4db2385aae0e88ffedfd55a2aa899752c28aec1a0","last_reissued_at":"2026-07-05T01:09:36.523352Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:09:36.523352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Online Learning Methodology for Performance Modeling of Graphics Processors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SP"],"primary_cat":"eess.SY","authors_text":"Francesco Paterna, Manoj Babu, Michael Kishinevsky, Raid Ayoub, Suat Gumussoy, Ujjwal Gupta, Umit Ogras","submitted_at":"2020-03-26T04:32:02Z","abstract_excerpt":"Approximately 18 percent of the 3.2 million smartphone applications rely on integrated graphics processing units (GPUs) to achieve competitive performance. Graphics performance, typically measured in frames per second, is a strong function of the GPU frequency, which in turn has a significant impact on mobile processor power consumption. Consequently, dynamic power management algorithms have to assess the performance sensitivity to the frequency accurately to choose the operating frequency of the GPU effectively. Since the impact of GPU frequency on performance varies rapidly over time, there "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.11740","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2003.11740/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":"2003.11740","created_at":"2026-07-05T01:09:36.523415+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.11740v1","created_at":"2026-07-05T01:09:36.523415+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.11740","created_at":"2026-07-05T01:09:36.523415+00:00"},{"alias_kind":"pith_short_12","alias_value":"7B2AJS6DXQYP","created_at":"2026-07-05T01:09:36.523415+00:00"},{"alias_kind":"pith_short_16","alias_value":"7B2AJS6DXQYPKSCI","created_at":"2026-07-05T01:09:36.523415+00:00"},{"alias_kind":"pith_short_8","alias_value":"7B2AJS6D","created_at":"2026-07-05T01:09:36.523415+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/7B2AJS6DXQYPKSCI27SNWI4FVL","json":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL.json","graph_json":"https://pith.science/api/pith-number/7B2AJS6DXQYPKSCI27SNWI4FVL/graph.json","events_json":"https://pith.science/api/pith-number/7B2AJS6DXQYPKSCI27SNWI4FVL/events.json","paper":"https://pith.science/paper/7B2AJS6D"},"agent_actions":{"view_html":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL","download_json":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL.json","view_paper":"https://pith.science/paper/7B2AJS6D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.11740&json=true","fetch_graph":"https://pith.science/api/pith-number/7B2AJS6DXQYPKSCI27SNWI4FVL/graph.json","fetch_events":"https://pith.science/api/pith-number/7B2AJS6DXQYPKSCI27SNWI4FVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL/action/storage_attestation","attest_author":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL/action/author_attestation","sign_citation":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL/action/citation_signature","submit_replication":"https://pith.science/pith/7B2AJS6DXQYPKSCI27SNWI4FVL/action/replication_record"}},"created_at":"2026-07-05T01:09:36.523415+00:00","updated_at":"2026-07-05T01:09:36.523415+00:00"}