{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZEESZI5SAQXHWQTFBW6QC2TIYO","short_pith_number":"pith:ZEESZI5S","schema_version":"1.0","canonical_sha256":"c9092ca3b2042e7b42650dbd016a68c3a70bacf6e2bcb85e7e79ef90fb256ff9","source":{"kind":"arxiv","id":"2508.06339","version":1},"attestation_state":"computed","paper":{"title":"Performant Unified GPU Kernels for Portable Singular Value Computation Across Hardware and Precision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MS"],"primary_cat":"cs.DC","authors_text":"Alan Edelman, Evelyne Ringoot, Rabab Alomairy, Valentin Churavy","submitted_at":"2025-08-08T14:14:13Z","abstract_excerpt":"This paper presents a portable, GPU-accelerated implementation of a QR-based singular value computation algorithm in Julia. The singular value ecomposition (SVD) is a fundamental numerical tool in scientific computing and machine learning, providing optimal low-rank matrix approximations. Its importance has increased even more in large-scale machine learning pipelines, including large language models (LLMs), where it enables low-rank adaptation (LoRA). The implemented algorithm is based on the classic two-stage QR reduction, consisting of successive matrix reduction to band form and bidiagonal"},"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":"2508.06339","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-08-08T14:14:13Z","cross_cats_sorted":["cs.MS"],"title_canon_sha256":"e4549d1a03cdd1141baa2926373885da37873328c990e1a6de93c41492ebd2c5","abstract_canon_sha256":"8c810597d33ab645fd17321c0c186b059a77e3f54309639e880f4bdfb8c07879"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:54.575536Z","signature_b64":"vGXNJVnzvWkTBEzjK7G7/1HsojzeF/+YDy2vE+Qarw9n8oJKToGy2pFJMDMPL6Y1DoemwKoiNEyT/fm+6KMUAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9092ca3b2042e7b42650dbd016a68c3a70bacf6e2bcb85e7e79ef90fb256ff9","last_reissued_at":"2026-07-05T11:50:54.575024Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:54.575024Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Performant Unified GPU Kernels for Portable Singular Value Computation Across Hardware and Precision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MS"],"primary_cat":"cs.DC","authors_text":"Alan Edelman, Evelyne Ringoot, Rabab Alomairy, Valentin Churavy","submitted_at":"2025-08-08T14:14:13Z","abstract_excerpt":"This paper presents a portable, GPU-accelerated implementation of a QR-based singular value computation algorithm in Julia. The singular value ecomposition (SVD) is a fundamental numerical tool in scientific computing and machine learning, providing optimal low-rank matrix approximations. Its importance has increased even more in large-scale machine learning pipelines, including large language models (LLMs), where it enables low-rank adaptation (LoRA). The implemented algorithm is based on the classic two-stage QR reduction, consisting of successive matrix reduction to band form and bidiagonal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.06339","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/2508.06339/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":"2508.06339","created_at":"2026-07-05T11:50:54.575092+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.06339v1","created_at":"2026-07-05T11:50:54.575092+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.06339","created_at":"2026-07-05T11:50:54.575092+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEESZI5SAQXH","created_at":"2026-07-05T11:50:54.575092+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEESZI5SAQXHWQTF","created_at":"2026-07-05T11:50:54.575092+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEESZI5S","created_at":"2026-07-05T11:50:54.575092+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/ZEESZI5SAQXHWQTFBW6QC2TIYO","json":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO.json","graph_json":"https://pith.science/api/pith-number/ZEESZI5SAQXHWQTFBW6QC2TIYO/graph.json","events_json":"https://pith.science/api/pith-number/ZEESZI5SAQXHWQTFBW6QC2TIYO/events.json","paper":"https://pith.science/paper/ZEESZI5S"},"agent_actions":{"view_html":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO","download_json":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO.json","view_paper":"https://pith.science/paper/ZEESZI5S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.06339&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEESZI5SAQXHWQTFBW6QC2TIYO/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEESZI5SAQXHWQTFBW6QC2TIYO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO/action/storage_attestation","attest_author":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO/action/author_attestation","sign_citation":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO/action/citation_signature","submit_replication":"https://pith.science/pith/ZEESZI5SAQXHWQTFBW6QC2TIYO/action/replication_record"}},"created_at":"2026-07-05T11:50:54.575092+00:00","updated_at":"2026-07-05T11:50:54.575092+00:00"}