{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JPGWHH2N4NKODYAHCW7KIBHL3X","short_pith_number":"pith:JPGWHH2N","schema_version":"1.0","canonical_sha256":"4bcd639f4de354e1e00715bea404ebddd7cfd8d7ece11c2ddfeaf72c43d4ee45","source":{"kind":"arxiv","id":"1904.07162","version":3},"attestation_state":"computed","paper":{"title":"Single Machine Graph Analytics on Massive Datasets Using Intel Optane DC Persistent Memory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"(2) Intel Corporation), Gurbinder Gill (1), Keshav Pingali (1) ((1) The University of Texas at Austin, Loc Hoang (1), Ramesh Peri (2), Roshan Dathathri (1)","submitted_at":"2019-04-15T16:20:58Z","abstract_excerpt":"Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable memory with higher density and lower cost than DRAM. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this paper, we present key runtime and algorithmic principles to consider when performing graph analytics on extreme-scale graphs on large-memory platforms of this sort.\n  To demonstrate the importance of these principles, we evaluate four existing shared-memory graph frameworks on large real-world web-crawls, using a machine with 6TB of Optane PMM. Our results"},"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":"1904.07162","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-04-15T16:20:58Z","cross_cats_sorted":[],"title_canon_sha256":"278cfbe8afb246a1305fb8f2f6faf0734bad0e65460a65b5ab74164015405fb2","abstract_canon_sha256":"5a739b2655d90cda5fdbdcf25afeed1b66052171b97ec200735376a593433d13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:43:00.428433Z","signature_b64":"vnt3vAsAf/ZBgIxtE8lBxqeqkGjG+utp+wL7kAgeNhsZBEKar93fMbRVYjiELrqe8dir43G7fzWyi4vxjf/1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4bcd639f4de354e1e00715bea404ebddd7cfd8d7ece11c2ddfeaf72c43d4ee45","last_reissued_at":"2026-07-05T00:43:00.428009Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:43:00.428009Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Single Machine Graph Analytics on Massive Datasets Using Intel Optane DC Persistent Memory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"(2) Intel Corporation), Gurbinder Gill (1), Keshav Pingali (1) ((1) The University of Texas at Austin, Loc Hoang (1), Ramesh Peri (2), Roshan Dathathri (1)","submitted_at":"2019-04-15T16:20:58Z","abstract_excerpt":"Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable memory with higher density and lower cost than DRAM. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this paper, we present key runtime and algorithmic principles to consider when performing graph analytics on extreme-scale graphs on large-memory platforms of this sort.\n  To demonstrate the importance of these principles, we evaluate four existing shared-memory graph frameworks on large real-world web-crawls, using a machine with 6TB of Optane PMM. Our results"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.07162","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/1904.07162/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":"1904.07162","created_at":"2026-07-05T00:43:00.428065+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.07162v3","created_at":"2026-07-05T00:43:00.428065+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.07162","created_at":"2026-07-05T00:43:00.428065+00:00"},{"alias_kind":"pith_short_12","alias_value":"JPGWHH2N4NKO","created_at":"2026-07-05T00:43:00.428065+00:00"},{"alias_kind":"pith_short_16","alias_value":"JPGWHH2N4NKODYAH","created_at":"2026-07-05T00:43:00.428065+00:00"},{"alias_kind":"pith_short_8","alias_value":"JPGWHH2N","created_at":"2026-07-05T00:43:00.428065+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.06503","citing_title":"System Evaluation of the Intel Optane Byte-addressable NVM","ref_index":2019,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X","json":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X.json","graph_json":"https://pith.science/api/pith-number/JPGWHH2N4NKODYAHCW7KIBHL3X/graph.json","events_json":"https://pith.science/api/pith-number/JPGWHH2N4NKODYAHCW7KIBHL3X/events.json","paper":"https://pith.science/paper/JPGWHH2N"},"agent_actions":{"view_html":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X","download_json":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X.json","view_paper":"https://pith.science/paper/JPGWHH2N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.07162&json=true","fetch_graph":"https://pith.science/api/pith-number/JPGWHH2N4NKODYAHCW7KIBHL3X/graph.json","fetch_events":"https://pith.science/api/pith-number/JPGWHH2N4NKODYAHCW7KIBHL3X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X/action/storage_attestation","attest_author":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X/action/author_attestation","sign_citation":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X/action/citation_signature","submit_replication":"https://pith.science/pith/JPGWHH2N4NKODYAHCW7KIBHL3X/action/replication_record"}},"created_at":"2026-07-05T00:43:00.428065+00:00","updated_at":"2026-07-05T00:43:00.428065+00:00"}