{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IRJTVWJWRABH2DFP5CWEP4A4HT","short_pith_number":"pith:IRJTVWJW","schema_version":"1.0","canonical_sha256":"44533ad93688027d0cafe8ac47f01c3cedf6fce41f318ade0808bf1bc6eee127","source":{"kind":"arxiv","id":"2111.04872","version":2},"attestation_state":"computed","paper":{"title":"Performance Evaluation of Python Parallel Programming Models: Charm4Py and mpi4py","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PF","cs.PL"],"primary_cat":"cs.DC","authors_text":"Jaemin Choi, Laxmikant V. Kale, Matthias Diener, Simeng Liu, Zane Fink","submitted_at":"2021-11-08T23:32:39Z","abstract_excerpt":"Python is rapidly becoming the lingua franca of machine learning and scientific computing. With the broad use of frameworks such as Numpy, SciPy, and TensorFlow, scientific computing and machine learning are seeing a productivity boost on systems without a requisite loss in performance. While high-performance libraries often provide adequate performance within a node, distributed computing is required to scale Python across nodes and make it genuinely competitive in large-scale high-performance computing. Many frameworks, such as Charm4Py, DaCe, Dask, Legate Numpy, mpi4py, and Ray, scale Pytho"},"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":"2111.04872","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2021-11-08T23:32:39Z","cross_cats_sorted":["cs.PF","cs.PL"],"title_canon_sha256":"3b9ba836186dbe59db133eb9767bb92a3d613128d6b2d1d7d3bd510b85c424ef","abstract_canon_sha256":"1175f746edad5cfb7292b20678031651265b18ef19384606e3cf27f99d345e37"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:46:29.425498Z","signature_b64":"QVrvvzgs9mH4qQMEzG/A8YGVuq7E4tLDkWcdWn3Hqdru0QeIR8cBt5tEAZkwMg96HFW2Wn8U2sI5IpoKwfH7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44533ad93688027d0cafe8ac47f01c3cedf6fce41f318ade0808bf1bc6eee127","last_reissued_at":"2026-07-05T06:46:29.425075Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:46:29.425075Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Performance Evaluation of Python Parallel Programming Models: Charm4Py and mpi4py","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PF","cs.PL"],"primary_cat":"cs.DC","authors_text":"Jaemin Choi, Laxmikant V. Kale, Matthias Diener, Simeng Liu, Zane Fink","submitted_at":"2021-11-08T23:32:39Z","abstract_excerpt":"Python is rapidly becoming the lingua franca of machine learning and scientific computing. With the broad use of frameworks such as Numpy, SciPy, and TensorFlow, scientific computing and machine learning are seeing a productivity boost on systems without a requisite loss in performance. While high-performance libraries often provide adequate performance within a node, distributed computing is required to scale Python across nodes and make it genuinely competitive in large-scale high-performance computing. Many frameworks, such as Charm4Py, DaCe, Dask, Legate Numpy, mpi4py, and Ray, scale Pytho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.04872","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/2111.04872/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":"2111.04872","created_at":"2026-07-05T06:46:29.425135+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.04872v2","created_at":"2026-07-05T06:46:29.425135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.04872","created_at":"2026-07-05T06:46:29.425135+00:00"},{"alias_kind":"pith_short_12","alias_value":"IRJTVWJWRABH","created_at":"2026-07-05T06:46:29.425135+00:00"},{"alias_kind":"pith_short_16","alias_value":"IRJTVWJWRABH2DFP","created_at":"2026-07-05T06:46:29.425135+00:00"},{"alias_kind":"pith_short_8","alias_value":"IRJTVWJW","created_at":"2026-07-05T06:46:29.425135+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/IRJTVWJWRABH2DFP5CWEP4A4HT","json":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT.json","graph_json":"https://pith.science/api/pith-number/IRJTVWJWRABH2DFP5CWEP4A4HT/graph.json","events_json":"https://pith.science/api/pith-number/IRJTVWJWRABH2DFP5CWEP4A4HT/events.json","paper":"https://pith.science/paper/IRJTVWJW"},"agent_actions":{"view_html":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT","download_json":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT.json","view_paper":"https://pith.science/paper/IRJTVWJW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.04872&json=true","fetch_graph":"https://pith.science/api/pith-number/IRJTVWJWRABH2DFP5CWEP4A4HT/graph.json","fetch_events":"https://pith.science/api/pith-number/IRJTVWJWRABH2DFP5CWEP4A4HT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT/action/storage_attestation","attest_author":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT/action/author_attestation","sign_citation":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT/action/citation_signature","submit_replication":"https://pith.science/pith/IRJTVWJWRABH2DFP5CWEP4A4HT/action/replication_record"}},"created_at":"2026-07-05T06:46:29.425135+00:00","updated_at":"2026-07-05T06:46:29.425135+00:00"}