{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:36F6RUAKL4TLOXYH6JVGL72ZY3","short_pith_number":"pith:36F6RUAK","canonical_record":{"source":{"id":"2207.04296","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-09T16:28:57Z","cross_cats_sorted":["cs.AI","cs.PL"],"title_canon_sha256":"16df3cfcba48aa6ac579542e475ac7f83b557b2b49edde394c70bb204dab0d3b","abstract_canon_sha256":"e1ba04868950f1e4fb487ec00db2eeef232bf430d9ec5ac68768598e878bb0c8"},"schema_version":"1.0"},"canonical_sha256":"df8be8d00a5f26b75f07f26a65ff59c6e69eb8963240fb7e8af7aba9e6933351","source":{"kind":"arxiv","id":"2207.04296","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.04296","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"arxiv_version","alias_value":"2207.04296v2","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04296","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"pith_short_12","alias_value":"36F6RUAKL4TL","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"pith_short_16","alias_value":"36F6RUAKL4TLOXYH","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"pith_short_8","alias_value":"36F6RUAK","created_at":"2026-07-05T05:11:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:36F6RUAKL4TLOXYH6JVGL72ZY3","target":"record","payload":{"canonical_record":{"source":{"id":"2207.04296","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-09T16:28:57Z","cross_cats_sorted":["cs.AI","cs.PL"],"title_canon_sha256":"16df3cfcba48aa6ac579542e475ac7f83b557b2b49edde394c70bb204dab0d3b","abstract_canon_sha256":"e1ba04868950f1e4fb487ec00db2eeef232bf430d9ec5ac68768598e878bb0c8"},"schema_version":"1.0"},"canonical_sha256":"df8be8d00a5f26b75f07f26a65ff59c6e69eb8963240fb7e8af7aba9e6933351","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:09.366812Z","signature_b64":"G/DG+akzTY8h5HQyeBIYTuy8qo+8IPGLDIUaO3njqGsAP56hpaeQog+djhdKPZuOHIPHYkaN2+YMcWPdrtPaBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df8be8d00a5f26b75f07f26a65ff59c6e69eb8963240fb7e8af7aba9e6933351","last_reissued_at":"2026-07-05T05:11:09.366343Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:09.366343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.04296","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:11:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FylvyfTLK7QGpgbYbdPgJIMXE8kMdkQztGh0NiLJNkClyCWHbJreVwFuZB7i5fQVdQIhJT3XzTpMNXnEkwFCBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T23:25:50.064061Z"},"content_sha256":"2d044adab2220d2b37f04f0d2914e4d096e5d088dfbca9137b52c3607d9e9e92","schema_version":"1.0","event_id":"sha256:2d044adab2220d2b37f04f0d2914e4d096e5d088dfbca9137b52c3607d9e9e92"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:36F6RUAKL4TLOXYH6JVGL72ZY3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TensorIR: An Abstraction for Automatic Tensorized Program Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.PL"],"primary_cat":"cs.LG","authors_text":"Bohan Hou, Cody Hao Yu, Hongyi Jin, Junru Shao, Lianmin Zheng, Ruihang Lai, Siyuan Feng, Tianqi Chen, Wuwei Lin, Yong Yu, Zihao Ye","submitted_at":"2022-07-09T16:28:57Z","abstract_excerpt":"Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional tensor computations. These new acceleration primitives, along with the emerging machine learning models, bring tremendous engineering challenges. In this paper, we present TensorIR, a compiler abstraction for optimizing programs with these tensor computation primitives. TensorIR generalizes the loop nest representation used in existing machine learning compilers to bring tensor computation as the first-class c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.04296","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/2207.04296/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:11:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CMpOcHs29mggqWNu3siGCv0wR4EwNcn3SkIol87BBBfgnTRLydF/VNlXMVyHE06tAt16ZgqyCr0ho7at0BxSBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T23:25:50.065010Z"},"content_sha256":"2538b73e287b0f3d2f67092e31fbd70824a0776ec9b7d4209a6357d38200115d","schema_version":"1.0","event_id":"sha256:2538b73e287b0f3d2f67092e31fbd70824a0776ec9b7d4209a6357d38200115d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/36F6RUAKL4TLOXYH6JVGL72ZY3/bundle.json","state_url":"https://pith.science/pith/36F6RUAKL4TLOXYH6JVGL72ZY3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/36F6RUAKL4TLOXYH6JVGL72ZY3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T23:25:50Z","links":{"resolver":"https://pith.science/pith/36F6RUAKL4TLOXYH6JVGL72ZY3","bundle":"https://pith.science/pith/36F6RUAKL4TLOXYH6JVGL72ZY3/bundle.json","state":"https://pith.science/pith/36F6RUAKL4TLOXYH6JVGL72ZY3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/36F6RUAKL4TLOXYH6JVGL72ZY3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:36F6RUAKL4TLOXYH6JVGL72ZY3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e1ba04868950f1e4fb487ec00db2eeef232bf430d9ec5ac68768598e878bb0c8","cross_cats_sorted":["cs.AI","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-09T16:28:57Z","title_canon_sha256":"16df3cfcba48aa6ac579542e475ac7f83b557b2b49edde394c70bb204dab0d3b"},"schema_version":"1.0","source":{"id":"2207.04296","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.04296","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"arxiv_version","alias_value":"2207.04296v2","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04296","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"pith_short_12","alias_value":"36F6RUAKL4TL","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"pith_short_16","alias_value":"36F6RUAKL4TLOXYH","created_at":"2026-07-05T05:11:09Z"},{"alias_kind":"pith_short_8","alias_value":"36F6RUAK","created_at":"2026-07-05T05:11:09Z"}],"graph_snapshots":[{"event_id":"sha256:2538b73e287b0f3d2f67092e31fbd70824a0776ec9b7d4209a6357d38200115d","target":"graph","created_at":"2026-07-05T05:11:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2207.04296/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional tensor computations. These new acceleration primitives, along with the emerging machine learning models, bring tremendous engineering challenges. In this paper, we present TensorIR, a compiler abstraction for optimizing programs with these tensor computation primitives. TensorIR generalizes the loop nest representation used in existing machine learning compilers to bring tensor computation as the first-class c","authors_text":"Bohan Hou, Cody Hao Yu, Hongyi Jin, Junru Shao, Lianmin Zheng, Ruihang Lai, Siyuan Feng, Tianqi Chen, Wuwei Lin, Yong Yu, Zihao Ye","cross_cats":["cs.AI","cs.PL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-09T16:28:57Z","title":"TensorIR: An Abstraction for Automatic Tensorized Program Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.04296","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2d044adab2220d2b37f04f0d2914e4d096e5d088dfbca9137b52c3607d9e9e92","target":"record","created_at":"2026-07-05T05:11:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e1ba04868950f1e4fb487ec00db2eeef232bf430d9ec5ac68768598e878bb0c8","cross_cats_sorted":["cs.AI","cs.PL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-07-09T16:28:57Z","title_canon_sha256":"16df3cfcba48aa6ac579542e475ac7f83b557b2b49edde394c70bb204dab0d3b"},"schema_version":"1.0","source":{"id":"2207.04296","kind":"arxiv","version":2}},"canonical_sha256":"df8be8d00a5f26b75f07f26a65ff59c6e69eb8963240fb7e8af7aba9e6933351","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df8be8d00a5f26b75f07f26a65ff59c6e69eb8963240fb7e8af7aba9e6933351","first_computed_at":"2026-07-05T05:11:09.366343Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:11:09.366343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G/DG+akzTY8h5HQyeBIYTuy8qo+8IPGLDIUaO3njqGsAP56hpaeQog+djhdKPZuOHIPHYkaN2+YMcWPdrtPaBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:11:09.366812Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.04296","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d044adab2220d2b37f04f0d2914e4d096e5d088dfbca9137b52c3607d9e9e92","sha256:2538b73e287b0f3d2f67092e31fbd70824a0776ec9b7d4209a6357d38200115d"],"state_sha256":"1b639a81b30ec607b79b0eb00ef996b57238b83bd71f0a729b9a3ed66dc9ac47"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fujkPesmBkCZhFeqi3lU12YrGtfQ2LNQ3kt2HdMs2/nhHQE2mYBCaRJs+kPaX5s3NedifAH16pR+WpAUJqz/BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T23:25:50.070529Z","bundle_sha256":"eb66a9c20ec9cb4058f7ee485318cf8290e586e511ac4c841affe44bab603c8b"}}