{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:3JXGKYFLY4YNEHYAWUN6PJOTOG","short_pith_number":"pith:3JXGKYFL","schema_version":"1.0","canonical_sha256":"da6e6560abc730d21f00b51be7a5d371abd6cfe7e926cbb3c5051dd0ddc28505","source":{"kind":"arxiv","id":"1401.7494","version":1},"attestation_state":"computed","paper":{"title":"Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.DC","authors_text":"Georg Hager, Gerhard Wellein, Jan Treibig, Johannes Hofmann","submitted_at":"2014-01-29T12:41:44Z","abstract_excerpt":"Single Instruction, Multiple Data (SIMD) vectorization is a major driver of performance in current architectures, and is mandatory for achieving good performance with codes that are limited by instruction throughput. We investigate the efficiency of different SIMD-vectorized implementations of the RabbitCT benchmark. RabbitCT performs 3D image reconstruction by back projection, a vital operation in computed tomography applications. The underlying algorithm is a challenge for vectorization because it consists, apart from a streaming part, also of a bilinear interpolation requiring scattered acc"},"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":"1401.7494","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2014-01-29T12:41:44Z","cross_cats_sorted":["cs.PF"],"title_canon_sha256":"96f4117e3eda2484a58e8b6877bc3f0e623f33e782b81557de6d1b038ccda0c2","abstract_canon_sha256":"2e4dc9366ea07b4ff5ca741ee5391af110e53b61b30a951c9a8de32dbc83f34c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:00:48.198750Z","signature_b64":"LU3UfPPZCGsWMXZOQ3W83cRhYkJW3P6D1ynVuIo1rp2tuhZvArINLpBx5y0Vx8ml5Lbsaf5xl4z6dDvVa5JSBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da6e6560abc730d21f00b51be7a5d371abd6cfe7e926cbb3c5051dd0ddc28505","last_reissued_at":"2026-05-18T03:00:48.198094Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:00:48.198094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.PF"],"primary_cat":"cs.DC","authors_text":"Georg Hager, Gerhard Wellein, Jan Treibig, Johannes Hofmann","submitted_at":"2014-01-29T12:41:44Z","abstract_excerpt":"Single Instruction, Multiple Data (SIMD) vectorization is a major driver of performance in current architectures, and is mandatory for achieving good performance with codes that are limited by instruction throughput. We investigate the efficiency of different SIMD-vectorized implementations of the RabbitCT benchmark. RabbitCT performs 3D image reconstruction by back projection, a vital operation in computed tomography applications. The underlying algorithm is a challenge for vectorization because it consists, apart from a streaming part, also of a bilinear interpolation requiring scattered acc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.7494","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":"1401.7494","created_at":"2026-05-18T03:00:48.198206+00:00"},{"alias_kind":"arxiv_version","alias_value":"1401.7494v1","created_at":"2026-05-18T03:00:48.198206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.7494","created_at":"2026-05-18T03:00:48.198206+00:00"},{"alias_kind":"pith_short_12","alias_value":"3JXGKYFLY4YN","created_at":"2026-05-18T12:28:11.866339+00:00"},{"alias_kind":"pith_short_16","alias_value":"3JXGKYFLY4YNEHYA","created_at":"2026-05-18T12:28:11.866339+00:00"},{"alias_kind":"pith_short_8","alias_value":"3JXGKYFL","created_at":"2026-05-18T12:28:11.866339+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/3JXGKYFLY4YNEHYAWUN6PJOTOG","json":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG.json","graph_json":"https://pith.science/api/pith-number/3JXGKYFLY4YNEHYAWUN6PJOTOG/graph.json","events_json":"https://pith.science/api/pith-number/3JXGKYFLY4YNEHYAWUN6PJOTOG/events.json","paper":"https://pith.science/paper/3JXGKYFL"},"agent_actions":{"view_html":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG","download_json":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG.json","view_paper":"https://pith.science/paper/3JXGKYFL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1401.7494&json=true","fetch_graph":"https://pith.science/api/pith-number/3JXGKYFLY4YNEHYAWUN6PJOTOG/graph.json","fetch_events":"https://pith.science/api/pith-number/3JXGKYFLY4YNEHYAWUN6PJOTOG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG/action/storage_attestation","attest_author":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG/action/author_attestation","sign_citation":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG/action/citation_signature","submit_replication":"https://pith.science/pith/3JXGKYFLY4YNEHYAWUN6PJOTOG/action/replication_record"}},"created_at":"2026-05-18T03:00:48.198206+00:00","updated_at":"2026-05-18T03:00:48.198206+00:00"}