{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RISYQCCMTCELN3CSVOXTO7QPJ7","short_pith_number":"pith:RISYQCCM","schema_version":"1.0","canonical_sha256":"8a2588084c9888b6ec52abaf377e0f4fca16b4353142903c6c5bf7d42dfb5f94","source":{"kind":"arxiv","id":"1908.09207","version":1},"attestation_state":"computed","paper":{"title":"Demystifying the MLPerf Benchmark Suite","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bagus Hanindhito, Eugene B. John, Gunjan Jha, Lizy K. John, Qinzhe Wu, Ramesh Radhakrishnan, Snehil Verma","submitted_at":"2019-08-24T20:55:10Z","abstract_excerpt":"MLPerf, an emerging machine learning benchmark suite strives to cover a broad range of applications of machine learning. We present a study on its characteristics and how the MLPerf benchmarks differ from some of the previous deep learning benchmarks like DAWNBench and DeepBench. We find that application benchmarks such as MLPerf (although rich in kernels) exhibit different features compared to kernel benchmarks such as DeepBench. MLPerf benchmark suite contains a diverse set of models which allows unveiling various bottlenecks in the system. Based on our findings, dedicated low latency interc"},"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":"1908.09207","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-24T20:55:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"9dc339f09404d7e1b0d4ca2dac3cd583d98f0d457e4b56a7a33dc69b6f908d2d","abstract_canon_sha256":"c232b73ed4d7044fb0f16e23b721d086d10b824ae3a386e1c5935818764bf974"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:37.675633Z","signature_b64":"fBU/b8A2F/zlxnwh1+BpCC4B/80NkEzpMdF/y5xOekfMm9n9NuObYSBeMOxjPm1g5oUIloWnK0wMxBXtUpaeBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a2588084c9888b6ec52abaf377e0f4fca16b4353142903c6c5bf7d42dfb5f94","last_reissued_at":"2026-07-04T23:59:37.675193Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:37.675193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Demystifying the MLPerf Benchmark Suite","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bagus Hanindhito, Eugene B. John, Gunjan Jha, Lizy K. John, Qinzhe Wu, Ramesh Radhakrishnan, Snehil Verma","submitted_at":"2019-08-24T20:55:10Z","abstract_excerpt":"MLPerf, an emerging machine learning benchmark suite strives to cover a broad range of applications of machine learning. We present a study on its characteristics and how the MLPerf benchmarks differ from some of the previous deep learning benchmarks like DAWNBench and DeepBench. We find that application benchmarks such as MLPerf (although rich in kernels) exhibit different features compared to kernel benchmarks such as DeepBench. MLPerf benchmark suite contains a diverse set of models which allows unveiling various bottlenecks in the system. Based on our findings, dedicated low latency interc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09207","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.09207/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":"1908.09207","created_at":"2026-07-04T23:59:37.675251+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.09207v1","created_at":"2026-07-04T23:59:37.675251+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09207","created_at":"2026-07-04T23:59:37.675251+00:00"},{"alias_kind":"pith_short_12","alias_value":"RISYQCCMTCEL","created_at":"2026-07-04T23:59:37.675251+00:00"},{"alias_kind":"pith_short_16","alias_value":"RISYQCCMTCELN3CS","created_at":"2026-07-04T23:59:37.675251+00:00"},{"alias_kind":"pith_short_8","alias_value":"RISYQCCM","created_at":"2026-07-04T23:59:37.675251+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/RISYQCCMTCELN3CSVOXTO7QPJ7","json":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7.json","graph_json":"https://pith.science/api/pith-number/RISYQCCMTCELN3CSVOXTO7QPJ7/graph.json","events_json":"https://pith.science/api/pith-number/RISYQCCMTCELN3CSVOXTO7QPJ7/events.json","paper":"https://pith.science/paper/RISYQCCM"},"agent_actions":{"view_html":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7","download_json":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7.json","view_paper":"https://pith.science/paper/RISYQCCM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.09207&json=true","fetch_graph":"https://pith.science/api/pith-number/RISYQCCMTCELN3CSVOXTO7QPJ7/graph.json","fetch_events":"https://pith.science/api/pith-number/RISYQCCMTCELN3CSVOXTO7QPJ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7/action/storage_attestation","attest_author":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7/action/author_attestation","sign_citation":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7/action/citation_signature","submit_replication":"https://pith.science/pith/RISYQCCMTCELN3CSVOXTO7QPJ7/action/replication_record"}},"created_at":"2026-07-04T23:59:37.675251+00:00","updated_at":"2026-07-04T23:59:37.675251+00:00"}