{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:M4S6E4IG3LKRNDMP2ODW6YEAR2","short_pith_number":"pith:M4S6E4IG","schema_version":"1.0","canonical_sha256":"6725e27106dad5168d8fd3876f60808ea795e31f92698ba90d16d5eeba770180","source":{"kind":"arxiv","id":"2301.10936","version":2},"attestation_state":"computed","paper":{"title":"PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Chengruidong Zhang, Fan Yang, Huiqiang Jiang, Lidong Zhou, Lili Qiu, Lingxiao Ma, Mao Yang, Ningxin Zheng, Quanlu Zhang, Yuqing Yang, Zhenhua Han","submitted_at":"2023-01-26T04:50:14Z","abstract_excerpt":"Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor).\n  In this paper, we propose PIT, a deep-learning compiler for "},"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":"2301.10936","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-26T04:50:14Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"bf903dec07d6d63db4a8722210cceb23b412e588a2d0ecdce2ddfa440f5176c0","abstract_canon_sha256":"7207003e09c62efacdbe2cad2e611edc11df37eda073dd602788d0c444fda9fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:58:07.950530Z","signature_b64":"XCD58iRiW2+PLRRZFlYMhISsRGqwyqz8H3321VRYNdBg9JL28qVYAAipLQVeFeM+cF/MghYnR6e6XV8eR4USAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6725e27106dad5168d8fd3876f60808ea795e31f92698ba90d16d5eeba770180","last_reissued_at":"2026-07-05T06:58:07.950011Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:58:07.950011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Chengruidong Zhang, Fan Yang, Huiqiang Jiang, Lidong Zhou, Lili Qiu, Lingxiao Ma, Mao Yang, Ningxin Zheng, Quanlu Zhang, Yuqing Yang, Zhenhua Han","submitted_at":"2023-01-26T04:50:14Z","abstract_excerpt":"Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor).\n  In this paper, we propose PIT, a deep-learning compiler for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.10936","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/2301.10936/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":"2301.10936","created_at":"2026-07-05T06:58:07.950075+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.10936v2","created_at":"2026-07-05T06:58:07.950075+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.10936","created_at":"2026-07-05T06:58:07.950075+00:00"},{"alias_kind":"pith_short_12","alias_value":"M4S6E4IG3LKR","created_at":"2026-07-05T06:58:07.950075+00:00"},{"alias_kind":"pith_short_16","alias_value":"M4S6E4IG3LKRNDMP","created_at":"2026-07-05T06:58:07.950075+00:00"},{"alias_kind":"pith_short_8","alias_value":"M4S6E4IG","created_at":"2026-07-05T06:58:07.950075+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.01776","citing_title":"Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity","ref_index":71,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2","json":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2.json","graph_json":"https://pith.science/api/pith-number/M4S6E4IG3LKRNDMP2ODW6YEAR2/graph.json","events_json":"https://pith.science/api/pith-number/M4S6E4IG3LKRNDMP2ODW6YEAR2/events.json","paper":"https://pith.science/paper/M4S6E4IG"},"agent_actions":{"view_html":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2","download_json":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2.json","view_paper":"https://pith.science/paper/M4S6E4IG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.10936&json=true","fetch_graph":"https://pith.science/api/pith-number/M4S6E4IG3LKRNDMP2ODW6YEAR2/graph.json","fetch_events":"https://pith.science/api/pith-number/M4S6E4IG3LKRNDMP2ODW6YEAR2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2/action/storage_attestation","attest_author":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2/action/author_attestation","sign_citation":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2/action/citation_signature","submit_replication":"https://pith.science/pith/M4S6E4IG3LKRNDMP2ODW6YEAR2/action/replication_record"}},"created_at":"2026-07-05T06:58:07.950075+00:00","updated_at":"2026-07-05T06:58:07.950075+00:00"}