{"paper":{"title":"Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Distributed-memory interior-point methods deliver substantial speed-ups for block-angular energy system optimization problems.","cross_cats":[],"primary_cat":"math.OC","authors_text":"Annika Buchholz, Frederik Fiand, Janina Zittel, Lukas Mehl, Manuel Wetzel, Michael Bussieck, Niels Lindner, Thorsten Koch","submitted_at":"2026-05-06T07:53:55Z","abstract_excerpt":"Energy system optimization models are increasing in scope and resolution, yielding large and challenging linear programs. For a long time, the standard way to address such problems has relied on shared-memory interior-point methods (IPM), which combine robustness and accuracy but face scalability limits as model instance size grows. Recently, two promising directions for specialized solver architectures have emerged: (i) GPU-accelerated first-order methods (FOM); and (ii) distributed-memory IPM, which can exploit block structure that arises in many energy system models. This paper presents a c"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"distributed-memory IPM can leverage problem structure to deliver substantial speed-ups on specific problems with block-angular structures. GPU-accelerated FOMs demonstrate strong scalability but may yield solutions with higher relative infeasibilities, which, depending on the use case and model uncertainty, can still be acceptable.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the diverse test set of large-scale linear programs arising from energy system analysis is representative of real-world instances and that the observed performance differences and accuracy trade-offs generalize beyond the tested cases.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Distributed-memory IPMs deliver speed-ups on block-structured energy optimization problems while GPU FOMs scale well but produce solutions with higher infeasibility that may still be usable.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Distributed-memory interior-point methods deliver substantial speed-ups for block-angular energy system optimization problems.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6814294c5a15f98006dfb77e837fb9a709e624c2f3a25382bbccb54c332f1d5c"},"source":{"id":"2605.04605","kind":"arxiv","version":2},"verdict":{"id":"8d2a37b7-f9b2-4eae-98d8-8dce8e2fa26a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T16:58:41.724553Z","strongest_claim":"distributed-memory IPM can leverage problem structure to deliver substantial speed-ups on specific problems with block-angular structures. GPU-accelerated FOMs demonstrate strong scalability but may yield solutions with higher relative infeasibilities, which, depending on the use case and model uncertainty, can still be acceptable.","one_line_summary":"Distributed-memory IPMs deliver speed-ups on block-structured energy optimization problems while GPU FOMs scale well but produce solutions with higher infeasibility that may still be usable.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the diverse test set of large-scale linear programs arising from energy system analysis is representative of real-world instances and that the observed performance differences and accuracy trade-offs generalize beyond the tested cases.","pith_extraction_headline":"Distributed-memory interior-point methods deliver substantial speed-ups for block-angular energy system optimization problems."},"integrity":{"clean":false,"summary":{"advisory":0,"critical":1,"by_detector":{"doi_compliance":{"total":1,"advisory":0,"critical":1,"informational":0}},"informational":0},"endpoint":"/pith/2605.04605/integrity.json","findings":[{"note":"Identifier '10.1016/j.energy.2021' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","detector":"doi_compliance","severity":"critical","ref_index":3,"audited_at":"2026-05-19T14:18:48.452328Z","detected_doi":"10.1016/j.energy.2021","finding_type":"unresolvable_identifier","verdict_class":"cross_source","detected_arxiv_id":null}],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T11:37:06.712186Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T22:31:20.011164Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T14:18:48.452328Z","status":"completed","version":"1.0.0","findings_count":1}],"snapshot_sha256":"56f8856f8d35970490cf3e750bab35b3b46d856147caa5ac19006d5216937494"},"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"}