{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7QE2N2ODQD5UGZWJNRZR7RRD7I","short_pith_number":"pith:7QE2N2OD","schema_version":"1.0","canonical_sha256":"fc09a6e9c380fb4366c96c731fc623fa3958c2161cf47ab93f8649dde4b4a07d","source":{"kind":"arxiv","id":"2505.09375","version":2},"attestation_state":"computed","paper":{"title":"Strategies to Measure Energy Consumption Using RAPL During Workflow Execution on Commodity Clusters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Philipp Thamm, Ulf Leser","submitted_at":"2025-05-14T13:25:58Z","abstract_excerpt":"In science, problems in many fields can be solved by processing datasets using a series of computationally expensive algorithms, sometimes referred to as workflows. Traditionally, the configurations of these workflows are optimized to achieve a short runtime for the given task and dataset on a given (often distributed) infrastructure. However, recently more attention has been drawn to energy-efficient computing, due to the negative impact of energy-inefficient computing on the environment and energy costs. To be able to assess the energy-efficiency of a given workflow configuration, reliable a"},"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":"2505.09375","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-05-14T13:25:58Z","cross_cats_sorted":[],"title_canon_sha256":"07a1887093d9d2f5a0893e05fb0e4b0d3d53b22f6b69863a0a8cc21fe3f9f781","abstract_canon_sha256":"10f8a042534b50e70bbce55a449baf7b01fc454b88b0e4e906bf17c0e98c00c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:51.533786Z","signature_b64":"XXKgD7C1wgLJR6HWu3Xx2nN+L6aqHo11c5nXd/79OdeqsA1K7PdwfuGryIyDpm/nAHbj8Kc19Y+zPRLhJiWSCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc09a6e9c380fb4366c96c731fc623fa3958c2161cf47ab93f8649dde4b4a07d","last_reissued_at":"2026-07-05T11:04:51.533307Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:51.533307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Strategies to Measure Energy Consumption Using RAPL During Workflow Execution on Commodity Clusters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Philipp Thamm, Ulf Leser","submitted_at":"2025-05-14T13:25:58Z","abstract_excerpt":"In science, problems in many fields can be solved by processing datasets using a series of computationally expensive algorithms, sometimes referred to as workflows. Traditionally, the configurations of these workflows are optimized to achieve a short runtime for the given task and dataset on a given (often distributed) infrastructure. However, recently more attention has been drawn to energy-efficient computing, due to the negative impact of energy-inefficient computing on the environment and energy costs. To be able to assess the energy-efficiency of a given workflow configuration, reliable a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09375","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/2505.09375/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":"2505.09375","created_at":"2026-07-05T11:04:51.533382+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.09375v2","created_at":"2026-07-05T11:04:51.533382+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09375","created_at":"2026-07-05T11:04:51.533382+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QE2N2ODQD5U","created_at":"2026-07-05T11:04:51.533382+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QE2N2ODQD5UGZWJ","created_at":"2026-07-05T11:04:51.533382+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QE2N2OD","created_at":"2026-07-05T11:04:51.533382+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.22883","citing_title":"Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26815","citing_title":"What Is the Cost of Energy Monitoring? An Empirical Study on the Overhead of RAPL-Based Tools","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I","json":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I.json","graph_json":"https://pith.science/api/pith-number/7QE2N2ODQD5UGZWJNRZR7RRD7I/graph.json","events_json":"https://pith.science/api/pith-number/7QE2N2ODQD5UGZWJNRZR7RRD7I/events.json","paper":"https://pith.science/paper/7QE2N2OD"},"agent_actions":{"view_html":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I","download_json":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I.json","view_paper":"https://pith.science/paper/7QE2N2OD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.09375&json=true","fetch_graph":"https://pith.science/api/pith-number/7QE2N2ODQD5UGZWJNRZR7RRD7I/graph.json","fetch_events":"https://pith.science/api/pith-number/7QE2N2ODQD5UGZWJNRZR7RRD7I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I/action/storage_attestation","attest_author":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I/action/author_attestation","sign_citation":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I/action/citation_signature","submit_replication":"https://pith.science/pith/7QE2N2ODQD5UGZWJNRZR7RRD7I/action/replication_record"}},"created_at":"2026-07-05T11:04:51.533382+00:00","updated_at":"2026-07-05T11:04:51.533382+00:00"}