{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:KBCDMV2ZRZ7IXPTPCLPQN5HMEU","short_pith_number":"pith:KBCDMV2Z","canonical_record":{"source":{"id":"1804.04806","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-13T07:20:44Z","cross_cats_sorted":["cs.DC","cs.MS","cs.NE","stat.ML"],"title_canon_sha256":"ae2528a4467e9a28ef56263973beffaf61a39e6d48fd556a0338b2561e3662d7","abstract_canon_sha256":"cb4d274fa0040ceff0127e334233aeef03a09fe3dba650c7d54f5add46e50b5b"},"schema_version":"1.0"},"canonical_sha256":"50443657598e7e8bbe6f12df06f4ec25083d7b3124f9f16ab7f57f4efca2c3ef","source":{"kind":"arxiv","id":"1804.04806","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.04806","created_at":"2026-05-18T00:18:34Z"},{"alias_kind":"arxiv_version","alias_value":"1804.04806v1","created_at":"2026-05-18T00:18:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.04806","created_at":"2026-05-18T00:18:34Z"},{"alias_kind":"pith_short_12","alias_value":"KBCDMV2ZRZ7I","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"KBCDMV2ZRZ7IXPTP","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"KBCDMV2Z","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:KBCDMV2ZRZ7IXPTPCLPQN5HMEU","target":"record","payload":{"canonical_record":{"source":{"id":"1804.04806","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-13T07:20:44Z","cross_cats_sorted":["cs.DC","cs.MS","cs.NE","stat.ML"],"title_canon_sha256":"ae2528a4467e9a28ef56263973beffaf61a39e6d48fd556a0338b2561e3662d7","abstract_canon_sha256":"cb4d274fa0040ceff0127e334233aeef03a09fe3dba650c7d54f5add46e50b5b"},"schema_version":"1.0"},"canonical_sha256":"50443657598e7e8bbe6f12df06f4ec25083d7b3124f9f16ab7f57f4efca2c3ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:34.922247Z","signature_b64":"OVebcphbyI/+Z2rJ9Q5/IrDMGrS2WfKisCuPAvUs4+Ni2n2BMArzq1X291McEYzc1tgtoeb+WQYn4ixXLs/1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50443657598e7e8bbe6f12df06f4ec25083d7b3124f9f16ab7f57f4efca2c3ef","last_reissued_at":"2026-05-18T00:18:34.921733Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:34.921733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.04806","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:18:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yG/eMQxnnTa7miOFeBJ7vv6/rzW9S4kGeUFG3+LfdDuzpVnUHdA6YPXJrrL3qgxcNAXn5QyDCyCO4jPz3RbVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T02:35:36.531916Z"},"content_sha256":"6d0feec2eafe9267748f8c5ff0b46d03e2e0cb698014437cbc4b36b43b8c8117","schema_version":"1.0","event_id":"sha256:6d0feec2eafe9267748f8c5ff0b46d03e2e0cb698014437cbc4b36b43b8c8117"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:KBCDMV2ZRZ7IXPTPCLPQN5HMEU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"{\\mu}-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.MS","cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Satoshi Matsuoka, Tal Ben-Nun, Torsten Hoefler, Yosuke Oyama","submitted_at":"2018-04-13T07:20:44Z","abstract_excerpt":"NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN, the decision is performed on a per-layer basis, and thus it often resorts to slower algorithms that fit the workspace size constraints. We present {\\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides layers' mini-batch computation into several micro-batches. Based"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.04806","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":""},"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":"2026-05-18T00:18:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yqez/fPxDUo3yTBY6qFagjtie9CFI8QyKXjcpjxVdZDNcqNpe1SwKepSto/jjPxLd6ESp9A6EDQ7CobCrwQHBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T02:35:36.532445Z"},"content_sha256":"31b0617e03c923517c969fdef3dc64d3f49f5f070d2365776bcc69b36189dab0","schema_version":"1.0","event_id":"sha256:31b0617e03c923517c969fdef3dc64d3f49f5f070d2365776bcc69b36189dab0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KBCDMV2ZRZ7IXPTPCLPQN5HMEU/bundle.json","state_url":"https://pith.science/pith/KBCDMV2ZRZ7IXPTPCLPQN5HMEU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KBCDMV2ZRZ7IXPTPCLPQN5HMEU/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":"2026-06-01T02:35:36Z","links":{"resolver":"https://pith.science/pith/KBCDMV2ZRZ7IXPTPCLPQN5HMEU","bundle":"https://pith.science/pith/KBCDMV2ZRZ7IXPTPCLPQN5HMEU/bundle.json","state":"https://pith.science/pith/KBCDMV2ZRZ7IXPTPCLPQN5HMEU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KBCDMV2ZRZ7IXPTPCLPQN5HMEU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:KBCDMV2ZRZ7IXPTPCLPQN5HMEU","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":"cb4d274fa0040ceff0127e334233aeef03a09fe3dba650c7d54f5add46e50b5b","cross_cats_sorted":["cs.DC","cs.MS","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-13T07:20:44Z","title_canon_sha256":"ae2528a4467e9a28ef56263973beffaf61a39e6d48fd556a0338b2561e3662d7"},"schema_version":"1.0","source":{"id":"1804.04806","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.04806","created_at":"2026-05-18T00:18:34Z"},{"alias_kind":"arxiv_version","alias_value":"1804.04806v1","created_at":"2026-05-18T00:18:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.04806","created_at":"2026-05-18T00:18:34Z"},{"alias_kind":"pith_short_12","alias_value":"KBCDMV2ZRZ7I","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"KBCDMV2ZRZ7IXPTP","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"KBCDMV2Z","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:31b0617e03c923517c969fdef3dc64d3f49f5f070d2365776bcc69b36189dab0","target":"graph","created_at":"2026-05-18T00:18:34Z","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"},"paper":{"abstract_excerpt":"NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN, the decision is performed on a per-layer basis, and thus it often resorts to slower algorithms that fit the workspace size constraints. We present {\\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides layers' mini-batch computation into several micro-batches. Based","authors_text":"Satoshi Matsuoka, Tal Ben-Nun, Torsten Hoefler, Yosuke Oyama","cross_cats":["cs.DC","cs.MS","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-13T07:20:44Z","title":"{\\mu}-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.04806","kind":"arxiv","version":1},"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":"sha256:6d0feec2eafe9267748f8c5ff0b46d03e2e0cb698014437cbc4b36b43b8c8117","target":"record","created_at":"2026-05-18T00:18:34Z","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":"cb4d274fa0040ceff0127e334233aeef03a09fe3dba650c7d54f5add46e50b5b","cross_cats_sorted":["cs.DC","cs.MS","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-13T07:20:44Z","title_canon_sha256":"ae2528a4467e9a28ef56263973beffaf61a39e6d48fd556a0338b2561e3662d7"},"schema_version":"1.0","source":{"id":"1804.04806","kind":"arxiv","version":1}},"canonical_sha256":"50443657598e7e8bbe6f12df06f4ec25083d7b3124f9f16ab7f57f4efca2c3ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"50443657598e7e8bbe6f12df06f4ec25083d7b3124f9f16ab7f57f4efca2c3ef","first_computed_at":"2026-05-18T00:18:34.921733Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:18:34.921733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OVebcphbyI/+Z2rJ9Q5/IrDMGrS2WfKisCuPAvUs4+Ni2n2BMArzq1X291McEYzc1tgtoeb+WQYn4ixXLs/1Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:18:34.922247Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.04806","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d0feec2eafe9267748f8c5ff0b46d03e2e0cb698014437cbc4b36b43b8c8117","sha256:31b0617e03c923517c969fdef3dc64d3f49f5f070d2365776bcc69b36189dab0"],"state_sha256":"e3cdf69e9227b4d860489c17037056ed2203b68bb137cf4d80b571944b66ba11"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3equX9RWpEde4T/Rr5DHa50Ai2+xA8bjKTwnfYQ35nwuCNKg2Ik7EbiueS8wziq48mGx7C1Ft7ZCTDuXWGhaDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T02:35:36.535678Z","bundle_sha256":"87fdf1e3a2425b4061d830e2a5a6193faa5d01935f7f218a11d8f7845d092c1d"}}