{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ESEOWBMP2DXL6GUTM32HYJWWVY","short_pith_number":"pith:ESEOWBMP","schema_version":"1.0","canonical_sha256":"2488eb058fd0eebf1a9366f47c26d6ae394225c335e3adbc6022b3486123bbe2","source":{"kind":"arxiv","id":"2305.00798","version":1},"attestation_state":"computed","paper":{"title":"Performance and Energy Consumption of Parallel Machine Learning Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Preston Brazzle, Stephen Cahoon, Xidong Wu","submitted_at":"2023-05-01T13:04:39Z","abstract_excerpt":"Machine learning models have achieved remarkable success in various real-world applications such as data science, computer vision, and natural language processing. However, model training in machine learning requires large-scale data sets and multiple iterations before it can work properly. Parallelization of training algorithms is a common strategy to speed up the process of training. However, many studies on model training and inference focus only on aspects of performance. Power consumption is also an important metric for any type of computation, especially high-performance applications. Ma"},"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":"2305.00798","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2023-05-01T13:04:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dba26a0e12d9d387a37c851e809f7dc03b16a91d0a4c5c4cfb90854faf83e088","abstract_canon_sha256":"0d0b2efbe06c6e64625f5d2107e079b84ab13c59d40de282d0b3cc53c8ea0271"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:05:45.312464Z","signature_b64":"TuAywA4pHbmHeF6nFIzhea5JnWz8Sm/+jEWn/tzaBQqzDLJ8a+yAqoht8noCmwA10EZMFgN4qDbBi2n5RDcNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2488eb058fd0eebf1a9366f47c26d6ae394225c335e3adbc6022b3486123bbe2","last_reissued_at":"2026-07-05T06:05:45.312039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:05:45.312039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Performance and Energy Consumption of Parallel Machine Learning Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Preston Brazzle, Stephen Cahoon, Xidong Wu","submitted_at":"2023-05-01T13:04:39Z","abstract_excerpt":"Machine learning models have achieved remarkable success in various real-world applications such as data science, computer vision, and natural language processing. However, model training in machine learning requires large-scale data sets and multiple iterations before it can work properly. Parallelization of training algorithms is a common strategy to speed up the process of training. However, many studies on model training and inference focus only on aspects of performance. Power consumption is also an important metric for any type of computation, especially high-performance applications. Ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.00798","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/2305.00798/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":"2305.00798","created_at":"2026-07-05T06:05:45.312094+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.00798v1","created_at":"2026-07-05T06:05:45.312094+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.00798","created_at":"2026-07-05T06:05:45.312094+00:00"},{"alias_kind":"pith_short_12","alias_value":"ESEOWBMP2DXL","created_at":"2026-07-05T06:05:45.312094+00:00"},{"alias_kind":"pith_short_16","alias_value":"ESEOWBMP2DXL6GUT","created_at":"2026-07-05T06:05:45.312094+00:00"},{"alias_kind":"pith_short_8","alias_value":"ESEOWBMP","created_at":"2026-07-05T06:05:45.312094+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23546","citing_title":"The Energy Consumption of Transformer Fine-Tuning: A Roofline-Inspired Scaling Model","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY","json":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY.json","graph_json":"https://pith.science/api/pith-number/ESEOWBMP2DXL6GUTM32HYJWWVY/graph.json","events_json":"https://pith.science/api/pith-number/ESEOWBMP2DXL6GUTM32HYJWWVY/events.json","paper":"https://pith.science/paper/ESEOWBMP"},"agent_actions":{"view_html":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY","download_json":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY.json","view_paper":"https://pith.science/paper/ESEOWBMP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.00798&json=true","fetch_graph":"https://pith.science/api/pith-number/ESEOWBMP2DXL6GUTM32HYJWWVY/graph.json","fetch_events":"https://pith.science/api/pith-number/ESEOWBMP2DXL6GUTM32HYJWWVY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY/action/storage_attestation","attest_author":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY/action/author_attestation","sign_citation":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY/action/citation_signature","submit_replication":"https://pith.science/pith/ESEOWBMP2DXL6GUTM32HYJWWVY/action/replication_record"}},"created_at":"2026-07-05T06:05:45.312094+00:00","updated_at":"2026-07-05T06:05:45.312094+00:00"}