{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KIYQPRQHBXXF6EVNH3E6TZDSEL","short_pith_number":"pith:KIYQPRQH","schema_version":"1.0","canonical_sha256":"523107c6070dee5f12ad3ec9e9e47222e010da6f644628245e922d8ffc9b5d5c","source":{"kind":"arxiv","id":"2309.02521","version":3},"attestation_state":"computed","paper":{"title":"Comparative Analysis of CPU and GPU Profiling for Deep Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Dipesh Gyawali","submitted_at":"2023-09-05T18:22:11Z","abstract_excerpt":"Deep Learning(DL) and Machine Learning(ML) applications are rapidly increasing in recent days. Massive amounts of data are being generated over the internet which can derive meaningful results by the use of ML and DL algorithms. Hardware resources and open-source libraries have made it easy to implement these algorithms. Tensorflow and Pytorch are one of the leading frameworks for implementing ML projects. By using those frameworks, we can trace the operations executed on both GPU and CPU to analyze the resource allocations and consumption. This paper presents the time and memory allocation of"},"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":"2309.02521","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2023-09-05T18:22:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"eb1bde0646ec3d55598fc507cd6d52fd4fb7343680c14aaeffe110efb4e29fc0","abstract_canon_sha256":"19131a57bb65754600d5af66f567496d4756db96b1c1b3bdee254bff295f580d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:01.444748Z","signature_b64":"6iukKDwH4vIKwBQuYuPvotgE7ckgn1CjSq9nl7dQ7h6MQVZ6S6Q7TTKiwxo+xGUQUzd8yXAim5V6y75OIusHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"523107c6070dee5f12ad3ec9e9e47222e010da6f644628245e922d8ffc9b5d5c","last_reissued_at":"2026-07-05T07:22:01.444253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:01.444253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparative Analysis of CPU and GPU Profiling for Deep Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Dipesh Gyawali","submitted_at":"2023-09-05T18:22:11Z","abstract_excerpt":"Deep Learning(DL) and Machine Learning(ML) applications are rapidly increasing in recent days. Massive amounts of data are being generated over the internet which can derive meaningful results by the use of ML and DL algorithms. Hardware resources and open-source libraries have made it easy to implement these algorithms. Tensorflow and Pytorch are one of the leading frameworks for implementing ML projects. By using those frameworks, we can trace the operations executed on both GPU and CPU to analyze the resource allocations and consumption. This paper presents the time and memory allocation of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02521","kind":"arxiv","version":3},"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/2309.02521/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":"2309.02521","created_at":"2026-07-05T07:22:01.444308+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.02521v3","created_at":"2026-07-05T07:22:01.444308+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02521","created_at":"2026-07-05T07:22:01.444308+00:00"},{"alias_kind":"pith_short_12","alias_value":"KIYQPRQHBXXF","created_at":"2026-07-05T07:22:01.444308+00:00"},{"alias_kind":"pith_short_16","alias_value":"KIYQPRQHBXXF6EVN","created_at":"2026-07-05T07:22:01.444308+00:00"},{"alias_kind":"pith_short_8","alias_value":"KIYQPRQH","created_at":"2026-07-05T07:22:01.444308+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.05584","citing_title":"ProfilingAgent: Profiling-Guided Agentic Reasoning for Adaptive Model Optimization","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL","json":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL.json","graph_json":"https://pith.science/api/pith-number/KIYQPRQHBXXF6EVNH3E6TZDSEL/graph.json","events_json":"https://pith.science/api/pith-number/KIYQPRQHBXXF6EVNH3E6TZDSEL/events.json","paper":"https://pith.science/paper/KIYQPRQH"},"agent_actions":{"view_html":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL","download_json":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL.json","view_paper":"https://pith.science/paper/KIYQPRQH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.02521&json=true","fetch_graph":"https://pith.science/api/pith-number/KIYQPRQHBXXF6EVNH3E6TZDSEL/graph.json","fetch_events":"https://pith.science/api/pith-number/KIYQPRQHBXXF6EVNH3E6TZDSEL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL/action/storage_attestation","attest_author":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL/action/author_attestation","sign_citation":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL/action/citation_signature","submit_replication":"https://pith.science/pith/KIYQPRQHBXXF6EVNH3E6TZDSEL/action/replication_record"}},"created_at":"2026-07-05T07:22:01.444308+00:00","updated_at":"2026-07-05T07:22:01.444308+00:00"}