{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:O46YYYSQ7PL4RC6PNOZ2ST4LI6","short_pith_number":"pith:O46YYYSQ","schema_version":"1.0","canonical_sha256":"773d8c6250fbd7c88bcf6bb3a94f8b47a91a33ea65fc0f5ece96f9aa0cfde275","source":{"kind":"arxiv","id":"1812.10460","version":3},"attestation_state":"computed","paper":{"title":"CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","math.IT"],"primary_cat":"cs.IT","authors_text":"Mohammad Ali Maddah-Ali, Tayyebeh Jahani-Nezhad","submitted_at":"2018-12-26T18:52:41Z","abstract_excerpt":"In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined as the total number of worker nodes that we need to wait for to be able to recover the final result. To exploit the fact that only an approximated result is required, in reducing the recovery threshold, some sorts of pre-compression are required. However, compression inherently involves some randomness that would lose the structure of the matrices. On the other hand, considering t"},"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":"1812.10460","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-26T18:52:41Z","cross_cats_sorted":["cs.DC","math.IT"],"title_canon_sha256":"461a852bd29ec78789e039371c45ce57a8a4ddc29a0a83ce4c1164381ee1b6ac","abstract_canon_sha256":"dcbe4a3c4037078e49cd29936db90825c4fb1172cab1f5fcbccdc183b4b3dc8c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:14:39.898625Z","signature_b64":"ODTeDi4+F+DNwN8aDux33FJ0UwsNBczSXIqTf7rnQEwcYtlqBW5HcbXJpCJ6Up5gYFZkWQ6mN3tPudLecCWzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"773d8c6250fbd7c88bcf6bb3a94f8b47a91a33ea65fc0f5ece96f9aa0cfde275","last_reissued_at":"2026-07-05T02:14:39.898182Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:14:39.898182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","math.IT"],"primary_cat":"cs.IT","authors_text":"Mohammad Ali Maddah-Ali, Tayyebeh Jahani-Nezhad","submitted_at":"2018-12-26T18:52:41Z","abstract_excerpt":"In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined as the total number of worker nodes that we need to wait for to be able to recover the final result. To exploit the fact that only an approximated result is required, in reducing the recovery threshold, some sorts of pre-compression are required. However, compression inherently involves some randomness that would lose the structure of the matrices. On the other hand, considering t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10460","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1812.10460/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1812.10460","created_at":"2026-07-05T02:14:39.898243+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.10460v3","created_at":"2026-07-05T02:14:39.898243+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10460","created_at":"2026-07-05T02:14:39.898243+00:00"},{"alias_kind":"pith_short_12","alias_value":"O46YYYSQ7PL4","created_at":"2026-07-05T02:14:39.898243+00:00"},{"alias_kind":"pith_short_16","alias_value":"O46YYYSQ7PL4RC6P","created_at":"2026-07-05T02:14:39.898243+00:00"},{"alias_kind":"pith_short_8","alias_value":"O46YYYSQ","created_at":"2026-07-05T02:14:39.898243+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.04255","citing_title":"Secure Coded Multi-Party Computation for Massive Matrix Operations","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6","json":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6.json","graph_json":"https://pith.science/api/pith-number/O46YYYSQ7PL4RC6PNOZ2ST4LI6/graph.json","events_json":"https://pith.science/api/pith-number/O46YYYSQ7PL4RC6PNOZ2ST4LI6/events.json","paper":"https://pith.science/paper/O46YYYSQ"},"agent_actions":{"view_html":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6","download_json":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6.json","view_paper":"https://pith.science/paper/O46YYYSQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.10460&json=true","fetch_graph":"https://pith.science/api/pith-number/O46YYYSQ7PL4RC6PNOZ2ST4LI6/graph.json","fetch_events":"https://pith.science/api/pith-number/O46YYYSQ7PL4RC6PNOZ2ST4LI6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6/action/storage_attestation","attest_author":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6/action/author_attestation","sign_citation":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6/action/citation_signature","submit_replication":"https://pith.science/pith/O46YYYSQ7PL4RC6PNOZ2ST4LI6/action/replication_record"}},"created_at":"2026-07-05T02:14:39.898243+00:00","updated_at":"2026-07-05T02:14:39.898243+00:00"}