{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GSDWDBPEOYTWY2EWG7BDGRMVBW","short_pith_number":"pith:GSDWDBPE","schema_version":"1.0","canonical_sha256":"34876185e476276c689637c23345950d99f3ea7c75dfaf9e43f111141728afcd","source":{"kind":"arxiv","id":"1612.08060","version":3},"attestation_state":"computed","paper":{"title":"Node Aware Sparse Matrix-Vector Multiplication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MS"],"primary_cat":"cs.DC","authors_text":"Amanda Bienz, Luke N. Olson, William D. Gropp","submitted_at":"2016-12-23T18:40:46Z","abstract_excerpt":"The sparse matrix-vector multiply (SpMV) operation is a key computational kernel in many simulations and linear solvers. The large communication requirements associated with a reference implementation of a parallel SpMV result in poor parallel scalability. The cost of communication depends on the physical locations of the send and receive processes: messages injected into the network are more costly than messages sent between processes on the same node. In this paper, a node aware parallel SpMV (NAPSpMV) is introduced to exploit knowledge of the system topology, specifically the node-processor"},"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":"1612.08060","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2016-12-23T18:40:46Z","cross_cats_sorted":["cs.MS"],"title_canon_sha256":"3d97b59041a666975621cde6c41e5a74976e1d8bdecc1ff3fe804752ccef465a","abstract_canon_sha256":"8fd6de829990ba9683aa3ab38d466a37680a405aaa6795937746420eee7a022a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:30.645541Z","signature_b64":"jfDN/R+bmF847zmN+n0a1BBswTPFDcFudiE9U3mSpB0hBBqL3FHzMrPRWmSIi97335miIX8JpggMYDetscF/Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34876185e476276c689637c23345950d99f3ea7c75dfaf9e43f111141728afcd","last_reissued_at":"2026-05-18T00:30:30.644923Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:30.644923Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Node Aware Sparse Matrix-Vector Multiplication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MS"],"primary_cat":"cs.DC","authors_text":"Amanda Bienz, Luke N. Olson, William D. Gropp","submitted_at":"2016-12-23T18:40:46Z","abstract_excerpt":"The sparse matrix-vector multiply (SpMV) operation is a key computational kernel in many simulations and linear solvers. The large communication requirements associated with a reference implementation of a parallel SpMV result in poor parallel scalability. The cost of communication depends on the physical locations of the send and receive processes: messages injected into the network are more costly than messages sent between processes on the same node. In this paper, a node aware parallel SpMV (NAPSpMV) is introduced to exploit knowledge of the system topology, specifically the node-processor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.08060","kind":"arxiv","version":3},"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":"1612.08060","created_at":"2026-05-18T00:30:30.645015+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.08060v3","created_at":"2026-05-18T00:30:30.645015+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.08060","created_at":"2026-05-18T00:30:30.645015+00:00"},{"alias_kind":"pith_short_12","alias_value":"GSDWDBPEOYTW","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GSDWDBPEOYTWY2EW","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GSDWDBPE","created_at":"2026-05-18T12:30:19.053100+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.10613","citing_title":"Advances in Implementation, Theoretical Motivation, and Numerical Results for the Nested Iteration with Range Decomposition Algorithm","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW","json":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW.json","graph_json":"https://pith.science/api/pith-number/GSDWDBPEOYTWY2EWG7BDGRMVBW/graph.json","events_json":"https://pith.science/api/pith-number/GSDWDBPEOYTWY2EWG7BDGRMVBW/events.json","paper":"https://pith.science/paper/GSDWDBPE"},"agent_actions":{"view_html":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW","download_json":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW.json","view_paper":"https://pith.science/paper/GSDWDBPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.08060&json=true","fetch_graph":"https://pith.science/api/pith-number/GSDWDBPEOYTWY2EWG7BDGRMVBW/graph.json","fetch_events":"https://pith.science/api/pith-number/GSDWDBPEOYTWY2EWG7BDGRMVBW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW/action/storage_attestation","attest_author":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW/action/author_attestation","sign_citation":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW/action/citation_signature","submit_replication":"https://pith.science/pith/GSDWDBPEOYTWY2EWG7BDGRMVBW/action/replication_record"}},"created_at":"2026-05-18T00:30:30.645015+00:00","updated_at":"2026-05-18T00:30:30.645015+00:00"}