{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:EJTXEFJOPPY2DF24QEGFDR2GOG","short_pith_number":"pith:EJTXEFJO","schema_version":"1.0","canonical_sha256":"226772152e7bf1a1975c810c51c746718f66698ffe097cc6eb13601694ea7101","source":{"kind":"arxiv","id":"1805.07891","version":2},"attestation_state":"computed","paper":{"title":"Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.DC","authors_text":"Amar Phanishayee, Arvind Krishnamurthy, Jacob Nelson, Liang Luo, Luis Ceze","submitted_at":"2018-05-21T04:55:04Z","abstract_excerpt":"Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud. Larger DNN models and faster compute engines are shifting DDNN training bottlenecks from computation to communication. This paper characterizes DDNN training to precisely pinpoint these bottlenecks. We found that timely training requires high performance parameter servers (PSs) with optimized network stacks and gradient processing pipelines, as well as server and network hardware with balanced computation and communication resources. We therefore propose PHub, a hig"},"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":"1805.07891","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-05-21T04:55:04Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"df63e3fd8d9bc48ed6306e547449f15a41a62ae1cb6c3414ddabe248e61ba02d","abstract_canon_sha256":"304525075e909432d5f7d88b8746cf05a8cc1bd30f2e6b3480c280f799b94946"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:34:12.304179Z","signature_b64":"HIESDshYBs06wXN0c71T4VoLTiPUJ7W4Azr1DG53Hy0+xdz7Pv8J2VNqY4Slc7qZqUUNzHEo/eyNXqwGm4K/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"226772152e7bf1a1975c810c51c746718f66698ffe097cc6eb13601694ea7101","last_reissued_at":"2026-07-05T00:34:12.303761Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:34:12.303761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.DC","authors_text":"Amar Phanishayee, Arvind Krishnamurthy, Jacob Nelson, Liang Luo, Luis Ceze","submitted_at":"2018-05-21T04:55:04Z","abstract_excerpt":"Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud. Larger DNN models and faster compute engines are shifting DDNN training bottlenecks from computation to communication. This paper characterizes DDNN training to precisely pinpoint these bottlenecks. We found that timely training requires high performance parameter servers (PSs) with optimized network stacks and gradient processing pipelines, as well as server and network hardware with balanced computation and communication resources. We therefore propose PHub, a hig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07891","kind":"arxiv","version":2},"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/1805.07891/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":"1805.07891","created_at":"2026-07-05T00:34:12.303827+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.07891v2","created_at":"2026-07-05T00:34:12.303827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07891","created_at":"2026-07-05T00:34:12.303827+00:00"},{"alias_kind":"pith_short_12","alias_value":"EJTXEFJOPPY2","created_at":"2026-07-05T00:34:12.303827+00:00"},{"alias_kind":"pith_short_16","alias_value":"EJTXEFJOPPY2DF24","created_at":"2026-07-05T00:34:12.303827+00:00"},{"alias_kind":"pith_short_8","alias_value":"EJTXEFJO","created_at":"2026-07-05T00:34:12.303827+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/EJTXEFJOPPY2DF24QEGFDR2GOG","json":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG.json","graph_json":"https://pith.science/api/pith-number/EJTXEFJOPPY2DF24QEGFDR2GOG/graph.json","events_json":"https://pith.science/api/pith-number/EJTXEFJOPPY2DF24QEGFDR2GOG/events.json","paper":"https://pith.science/paper/EJTXEFJO"},"agent_actions":{"view_html":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG","download_json":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG.json","view_paper":"https://pith.science/paper/EJTXEFJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.07891&json=true","fetch_graph":"https://pith.science/api/pith-number/EJTXEFJOPPY2DF24QEGFDR2GOG/graph.json","fetch_events":"https://pith.science/api/pith-number/EJTXEFJOPPY2DF24QEGFDR2GOG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG/action/storage_attestation","attest_author":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG/action/author_attestation","sign_citation":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG/action/citation_signature","submit_replication":"https://pith.science/pith/EJTXEFJOPPY2DF24QEGFDR2GOG/action/replication_record"}},"created_at":"2026-07-05T00:34:12.303827+00:00","updated_at":"2026-07-05T00:34:12.303827+00:00"}