{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LMIG7JWD6ZF7TVYNKOLDUTX7HF","short_pith_number":"pith:LMIG7JWD","schema_version":"1.0","canonical_sha256":"5b106fa6c3f64bf9d70d53963a4eff396f46b81a64a5681be217444732ac3719","source":{"kind":"arxiv","id":"2508.10305","version":1},"attestation_state":"computed","paper":{"title":"GPZ: GPU-Accelerated Lossy Compressor for Particle Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Franck Cappello, Guanpeng Li, Hanqi Guo, Jiajun Huang, Jiannan Tian, Jinyang Liu, Kai Zhao, Longtao Zhang, Ruoyu Li, Sheng Di, Xin Liang, Yafan Huang, Zhuoxun Yang","submitted_at":"2025-08-14T03:23:32Z","abstract_excerpt":"Particle-based simulations and point-cloud applications generate massive, irregular datasets that challenge storage, I/O, and real-time analytics. Traditional compression techniques struggle with irregular particle distributions and GPU architectural constraints, often resulting in limited throughput and suboptimal compression ratios. In this paper, we present GPZ, a high-performance, error-bounded lossy compressor designed specifically for large-scale particle data on modern GPUs. GPZ employs a novel four-stage parallel pipeline that synergistically balances high compression efficiency with 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":"2508.10305","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2025-08-14T03:23:32Z","cross_cats_sorted":[],"title_canon_sha256":"99c946eaffcc88c4f656e9908dc6f6f7e8384d8dc8ff52939fdf640cad13ca2b","abstract_canon_sha256":"beff42111d1197be4e762de7772b4c5d15ee1663377f90e3dedddf6f1829b032"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:53.502612Z","signature_b64":"ZbIckVo4OE1iu0AF70i75MO9cwgSq7xpNvSDkAFVMpTKQ79flpMBbZ1C3eKoafc3/PmicGZ17hYCruV5eT1kBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b106fa6c3f64bf9d70d53963a4eff396f46b81a64a5681be217444732ac3719","last_reissued_at":"2026-07-05T11:53:53.502111Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:53.502111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GPZ: GPU-Accelerated Lossy Compressor for Particle Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Franck Cappello, Guanpeng Li, Hanqi Guo, Jiajun Huang, Jiannan Tian, Jinyang Liu, Kai Zhao, Longtao Zhang, Ruoyu Li, Sheng Di, Xin Liang, Yafan Huang, Zhuoxun Yang","submitted_at":"2025-08-14T03:23:32Z","abstract_excerpt":"Particle-based simulations and point-cloud applications generate massive, irregular datasets that challenge storage, I/O, and real-time analytics. Traditional compression techniques struggle with irregular particle distributions and GPU architectural constraints, often resulting in limited throughput and suboptimal compression ratios. In this paper, we present GPZ, a high-performance, error-bounded lossy compressor designed specifically for large-scale particle data on modern GPUs. GPZ employs a novel four-stage parallel pipeline that synergistically balances high compression efficiency with t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.10305","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/2508.10305/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":"2508.10305","created_at":"2026-07-05T11:53:53.502171+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.10305v1","created_at":"2026-07-05T11:53:53.502171+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.10305","created_at":"2026-07-05T11:53:53.502171+00:00"},{"alias_kind":"pith_short_12","alias_value":"LMIG7JWD6ZF7","created_at":"2026-07-05T11:53:53.502171+00:00"},{"alias_kind":"pith_short_16","alias_value":"LMIG7JWD6ZF7TVYN","created_at":"2026-07-05T11:53:53.502171+00:00"},{"alias_kind":"pith_short_8","alias_value":"LMIG7JWD","created_at":"2026-07-05T11:53:53.502171+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/LMIG7JWD6ZF7TVYNKOLDUTX7HF","json":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF.json","graph_json":"https://pith.science/api/pith-number/LMIG7JWD6ZF7TVYNKOLDUTX7HF/graph.json","events_json":"https://pith.science/api/pith-number/LMIG7JWD6ZF7TVYNKOLDUTX7HF/events.json","paper":"https://pith.science/paper/LMIG7JWD"},"agent_actions":{"view_html":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF","download_json":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF.json","view_paper":"https://pith.science/paper/LMIG7JWD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.10305&json=true","fetch_graph":"https://pith.science/api/pith-number/LMIG7JWD6ZF7TVYNKOLDUTX7HF/graph.json","fetch_events":"https://pith.science/api/pith-number/LMIG7JWD6ZF7TVYNKOLDUTX7HF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF/action/storage_attestation","attest_author":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF/action/author_attestation","sign_citation":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF/action/citation_signature","submit_replication":"https://pith.science/pith/LMIG7JWD6ZF7TVYNKOLDUTX7HF/action/replication_record"}},"created_at":"2026-07-05T11:53:53.502171+00:00","updated_at":"2026-07-05T11:53:53.502171+00:00"}