{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:PY5HICEQDRRD5PO755OETL4WMH","short_pith_number":"pith:PY5HICEQ","canonical_record":{"source":{"id":"1412.4825","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-12-15T22:27:57Z","cross_cats_sorted":["cs.MS"],"title_canon_sha256":"38559666bdc4e5356948dafae5bbec241e14fe5b939a9e94f2892ec1a544e1e9","abstract_canon_sha256":"e0aac59dd1f02fa43a67116918156dd693428c329a3bfe877b21561d1af62469"},"schema_version":"1.0"},"canonical_sha256":"7e3a7408901c623ebddfef5c49af9661d3075c13b03d57ff1dd81090a9b8d04c","source":{"kind":"arxiv","id":"1412.4825","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.4825","created_at":"2026-05-18T02:31:10Z"},{"alias_kind":"arxiv_version","alias_value":"1412.4825v1","created_at":"2026-05-18T02:31:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.4825","created_at":"2026-05-18T02:31:10Z"},{"alias_kind":"pith_short_12","alias_value":"PY5HICEQDRRD","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"PY5HICEQDRRD5PO7","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"PY5HICEQ","created_at":"2026-05-18T12:28:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:PY5HICEQDRRD5PO755OETL4WMH","target":"record","payload":{"canonical_record":{"source":{"id":"1412.4825","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-12-15T22:27:57Z","cross_cats_sorted":["cs.MS"],"title_canon_sha256":"38559666bdc4e5356948dafae5bbec241e14fe5b939a9e94f2892ec1a544e1e9","abstract_canon_sha256":"e0aac59dd1f02fa43a67116918156dd693428c329a3bfe877b21561d1af62469"},"schema_version":"1.0"},"canonical_sha256":"7e3a7408901c623ebddfef5c49af9661d3075c13b03d57ff1dd81090a9b8d04c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:31:10.392276Z","signature_b64":"p2xp+OH33eyAhkRRDHZ3jZ+kCz/JDlvt2PAEsRFB03C/ffbX99Sut95yKcWoCnNvTBVByK02UZDTPZ3s3HQnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e3a7408901c623ebddfef5c49af9661d3075c13b03d57ff1dd81090a9b8d04c","last_reissued_at":"2026-05-18T02:31:10.391751Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:31:10.391751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1412.4825","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:31:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FGcRlg1kpEXWHgCWX7BIROE6nLF2CN5UtI74KgA/WLEKG/xwOR64nNOMmWRM0PBOX+7W01Gd5n3vLgJuqR+pCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T16:45:49.293071Z"},"content_sha256":"e4d440c0466914345adab5333be78a6685049606a76484c8f9807fbfcf0d1f93","schema_version":"1.0","event_id":"sha256:e4d440c0466914345adab5333be78a6685049606a76484c8f9807fbfcf0d1f93"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:PY5HICEQDRRD5PO755OETL4WMH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient SIMD RNG for Varying-Parameter Streams: C++ Class BatchRNG","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MS"],"primary_cat":"stat.CO","authors_text":"Alireza S. Mahani, Mansour T.A. Sharabiani","submitted_at":"2014-12-15T22:27:57Z","abstract_excerpt":"Single-Instruction, Multiple-Data (SIMD) random number generators (RNGs) take advantage of vector units to offer significant performance gain over non-vectorized libraries, but they often rely on batch production of deviates from distributions with fixed parameters. In many statistical applications such as Gibbs sampling, parameters of sampled distributions change from one iteration to the next, requiring that random deviates be generated one-at-a-time. This situation can render vectorized RNGs inefficient, and even inferior to their scalar counterparts. The C++ class BatchRNG uses buffers of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.4825","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:31:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RVadsPzFyKqOLnMzOfbf+qzTtZtEjLy39E0NITYmcNe9VtCGu1r9mERAWTYQeiPOF4wiWcSQzC/jQbWmQkC1CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T16:45:49.293818Z"},"content_sha256":"4554f3d770c223ced2be71bb1977f63a2ebba270aa4084e354f74f4422ad42ec","schema_version":"1.0","event_id":"sha256:4554f3d770c223ced2be71bb1977f63a2ebba270aa4084e354f74f4422ad42ec"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PY5HICEQDRRD5PO755OETL4WMH/bundle.json","state_url":"https://pith.science/pith/PY5HICEQDRRD5PO755OETL4WMH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PY5HICEQDRRD5PO755OETL4WMH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T16:45:49Z","links":{"resolver":"https://pith.science/pith/PY5HICEQDRRD5PO755OETL4WMH","bundle":"https://pith.science/pith/PY5HICEQDRRD5PO755OETL4WMH/bundle.json","state":"https://pith.science/pith/PY5HICEQDRRD5PO755OETL4WMH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PY5HICEQDRRD5PO755OETL4WMH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:PY5HICEQDRRD5PO755OETL4WMH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e0aac59dd1f02fa43a67116918156dd693428c329a3bfe877b21561d1af62469","cross_cats_sorted":["cs.MS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-12-15T22:27:57Z","title_canon_sha256":"38559666bdc4e5356948dafae5bbec241e14fe5b939a9e94f2892ec1a544e1e9"},"schema_version":"1.0","source":{"id":"1412.4825","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.4825","created_at":"2026-05-18T02:31:10Z"},{"alias_kind":"arxiv_version","alias_value":"1412.4825v1","created_at":"2026-05-18T02:31:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.4825","created_at":"2026-05-18T02:31:10Z"},{"alias_kind":"pith_short_12","alias_value":"PY5HICEQDRRD","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"PY5HICEQDRRD5PO7","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"PY5HICEQ","created_at":"2026-05-18T12:28:43Z"}],"graph_snapshots":[{"event_id":"sha256:4554f3d770c223ced2be71bb1977f63a2ebba270aa4084e354f74f4422ad42ec","target":"graph","created_at":"2026-05-18T02:31:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Single-Instruction, Multiple-Data (SIMD) random number generators (RNGs) take advantage of vector units to offer significant performance gain over non-vectorized libraries, but they often rely on batch production of deviates from distributions with fixed parameters. In many statistical applications such as Gibbs sampling, parameters of sampled distributions change from one iteration to the next, requiring that random deviates be generated one-at-a-time. This situation can render vectorized RNGs inefficient, and even inferior to their scalar counterparts. The C++ class BatchRNG uses buffers of ","authors_text":"Alireza S. Mahani, Mansour T.A. Sharabiani","cross_cats":["cs.MS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-12-15T22:27:57Z","title":"Efficient SIMD RNG for Varying-Parameter Streams: C++ Class BatchRNG"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.4825","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e4d440c0466914345adab5333be78a6685049606a76484c8f9807fbfcf0d1f93","target":"record","created_at":"2026-05-18T02:31:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e0aac59dd1f02fa43a67116918156dd693428c329a3bfe877b21561d1af62469","cross_cats_sorted":["cs.MS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-12-15T22:27:57Z","title_canon_sha256":"38559666bdc4e5356948dafae5bbec241e14fe5b939a9e94f2892ec1a544e1e9"},"schema_version":"1.0","source":{"id":"1412.4825","kind":"arxiv","version":1}},"canonical_sha256":"7e3a7408901c623ebddfef5c49af9661d3075c13b03d57ff1dd81090a9b8d04c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7e3a7408901c623ebddfef5c49af9661d3075c13b03d57ff1dd81090a9b8d04c","first_computed_at":"2026-05-18T02:31:10.391751Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:31:10.391751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p2xp+OH33eyAhkRRDHZ3jZ+kCz/JDlvt2PAEsRFB03C/ffbX99Sut95yKcWoCnNvTBVByK02UZDTPZ3s3HQnAA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:31:10.392276Z","signed_message":"canonical_sha256_bytes"},"source_id":"1412.4825","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e4d440c0466914345adab5333be78a6685049606a76484c8f9807fbfcf0d1f93","sha256:4554f3d770c223ced2be71bb1977f63a2ebba270aa4084e354f74f4422ad42ec"],"state_sha256":"21b6a97c7bf59c4d90202bb20402940321dd4bd48b6bf2fae03eb75070110827"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wKrcLcfbY4d2Y6PUE24vvKvMisxByYvdehMTVOMXBf19k/JsJGbav6AKMKiKJyiH2phXtqM7LcIBvn9C4KfJAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T16:45:49.298335Z","bundle_sha256":"53aef51d161e4e453deeb5b760fed2256bad5375eada06b07f1c4c94a8766096"}}