{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VM4KHAJER5YJY7XBHMAREZ55HC","short_pith_number":"pith:VM4KHAJE","schema_version":"1.0","canonical_sha256":"ab38a381248f709c7ee13b011267bd38a1860225d12a1bb74a5c789d78f4dad1","source":{"kind":"arxiv","id":"1609.08114","version":1},"attestation_state":"computed","paper":{"title":"Solving Batched Linear Programs on GPU and Multicore CPU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Amit Gurung, Rajarshi Ray","submitted_at":"2016-09-26T18:51:09Z","abstract_excerpt":"Linear Programs (LPs) appear in a large number of applications and offloading them to the GPU is viable to gain performance. Existing work on offloading and solving an LP on GPU suggests that performance is gained from large sized LPs (typically 500 constraints, 500 variables and above). In order to gain performance from GPU for applications involving small to medium sized LPs, we propose batched solving of a large number of LPs in parallel. In this paper, we present the design and CUDA implementation of our batched LP solver library, keeping memory coalescent access, reduced CPU-GPU memory tr"},"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":"1609.08114","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-09-26T18:51:09Z","cross_cats_sorted":[],"title_canon_sha256":"84f08f0c4e3172e3c594ef2c0059f0ad4810c0682092dd7e8ee3fedf8c44fbe0","abstract_canon_sha256":"b5345b59ed0e93ec88c4ba4f48b7904151dd4cf4736a9cec6436af33bb35b228"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:54.409231Z","signature_b64":"xR4qdgmuRobPhXn2vTS30u98kOQV+y5gHah3w3t6/HlB5DkQgLkPZPFPgS9RsGnzfCaex+lkydVNi5EuzWjPAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab38a381248f709c7ee13b011267bd38a1860225d12a1bb74a5c789d78f4dad1","last_reissued_at":"2026-05-18T01:03:54.408560Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:54.408560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving Batched Linear Programs on GPU and Multicore CPU","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Amit Gurung, Rajarshi Ray","submitted_at":"2016-09-26T18:51:09Z","abstract_excerpt":"Linear Programs (LPs) appear in a large number of applications and offloading them to the GPU is viable to gain performance. Existing work on offloading and solving an LP on GPU suggests that performance is gained from large sized LPs (typically 500 constraints, 500 variables and above). In order to gain performance from GPU for applications involving small to medium sized LPs, we propose batched solving of a large number of LPs in parallel. In this paper, we present the design and CUDA implementation of our batched LP solver library, keeping memory coalescent access, reduced CPU-GPU memory tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.08114","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":""},"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":"1609.08114","created_at":"2026-05-18T01:03:54.408672+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.08114v1","created_at":"2026-05-18T01:03:54.408672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.08114","created_at":"2026-05-18T01:03:54.408672+00:00"},{"alias_kind":"pith_short_12","alias_value":"VM4KHAJER5YJ","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VM4KHAJER5YJY7XB","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VM4KHAJE","created_at":"2026-05-18T12:30:48.956258+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/VM4KHAJER5YJY7XBHMAREZ55HC","json":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC.json","graph_json":"https://pith.science/api/pith-number/VM4KHAJER5YJY7XBHMAREZ55HC/graph.json","events_json":"https://pith.science/api/pith-number/VM4KHAJER5YJY7XBHMAREZ55HC/events.json","paper":"https://pith.science/paper/VM4KHAJE"},"agent_actions":{"view_html":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC","download_json":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC.json","view_paper":"https://pith.science/paper/VM4KHAJE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.08114&json=true","fetch_graph":"https://pith.science/api/pith-number/VM4KHAJER5YJY7XBHMAREZ55HC/graph.json","fetch_events":"https://pith.science/api/pith-number/VM4KHAJER5YJY7XBHMAREZ55HC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC/action/storage_attestation","attest_author":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC/action/author_attestation","sign_citation":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC/action/citation_signature","submit_replication":"https://pith.science/pith/VM4KHAJER5YJY7XBHMAREZ55HC/action/replication_record"}},"created_at":"2026-05-18T01:03:54.408672+00:00","updated_at":"2026-05-18T01:03:54.408672+00:00"}