{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:JLBKPJ76DG5WLPAVBIARUNFKIG","short_pith_number":"pith:JLBKPJ76","canonical_record":{"source":{"id":"1708.02983","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-08-09T19:49:13Z","cross_cats_sorted":[],"title_canon_sha256":"8fa504cecf0ff8d985027679f5b21f9e23bb705eb568cdd78dcaf9401abfd795","abstract_canon_sha256":"1150f9261a2352399584cdfe2493ef1ef48710fb21ac541d6200205d4799e9ec"},"schema_version":"1.0"},"canonical_sha256":"4ac2a7a7fe19bb65bc150a011a34aa41bf85189ecaf61bc363396a3c2c5d6820","source":{"kind":"arxiv","id":"1708.02983","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.02983","created_at":"2026-05-18T00:38:17Z"},{"alias_kind":"arxiv_version","alias_value":"1708.02983v1","created_at":"2026-05-18T00:38:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02983","created_at":"2026-05-18T00:38:17Z"},{"alias_kind":"pith_short_12","alias_value":"JLBKPJ76DG5W","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JLBKPJ76DG5WLPAV","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JLBKPJ76","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:JLBKPJ76DG5WLPAVBIARUNFKIG","target":"record","payload":{"canonical_record":{"source":{"id":"1708.02983","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-08-09T19:49:13Z","cross_cats_sorted":[],"title_canon_sha256":"8fa504cecf0ff8d985027679f5b21f9e23bb705eb568cdd78dcaf9401abfd795","abstract_canon_sha256":"1150f9261a2352399584cdfe2493ef1ef48710fb21ac541d6200205d4799e9ec"},"schema_version":"1.0"},"canonical_sha256":"4ac2a7a7fe19bb65bc150a011a34aa41bf85189ecaf61bc363396a3c2c5d6820","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:17.555111Z","signature_b64":"XRTYtpkP7wzx47/BS+3nEhVn+HYEtOYwW1F2aKKWSvV+UUxUZQfrvP/HIlHpxnXPXxIvBp/Pyac6CTuk1Z32Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ac2a7a7fe19bb65bc150a011a34aa41bf85189ecaf61bc363396a3c2c5d6820","last_reissued_at":"2026-05-18T00:38:17.554377Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:17.554377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.02983","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":"2026-05-18T00:38:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zr2Wq7O7tAaOBaKNny9Fvqluvxfpk1Q9kxWZN2ln7E4u2xsANnY8OztGlI9CyLzDu62Jt5bjLt/CaTnABImADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T06:27:06.260029Z"},"content_sha256":"2b3820890854fef1b0bada8854b6cdd4006d4b5dad14bd00bd10d38bd48f7f8c","schema_version":"1.0","event_id":"sha256:2b3820890854fef1b0bada8854b6cdd4006d4b5dad14bd00bd10d38bd48f7f8c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:JLBKPJ76DG5WLPAVBIARUNFKIG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scaling Deep Learning on GPU and Knights Landing clusters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Aydin Buluc, James Demmel, Yang You","submitted_at":"2017-08-09T19:49:13Z","abstract_excerpt":"The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02983","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":"2026-05-18T00:38:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"thPWmUmCRQDQaz5lx7VKCBaqc+cNjDU4DatlQfFsRHDPVUPc7Vcb+pu48yio19afrAhca7HDe83E0ZvhY0xxAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T06:27:06.260384Z"},"content_sha256":"af8fa637955ad5b048e52a19991717fbacf0b9ff4935dbe08a52665912eb80a4","schema_version":"1.0","event_id":"sha256:af8fa637955ad5b048e52a19991717fbacf0b9ff4935dbe08a52665912eb80a4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JLBKPJ76DG5WLPAVBIARUNFKIG/bundle.json","state_url":"https://pith.science/pith/JLBKPJ76DG5WLPAVBIARUNFKIG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JLBKPJ76DG5WLPAVBIARUNFKIG/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":"2026-05-28T06:27:06Z","links":{"resolver":"https://pith.science/pith/JLBKPJ76DG5WLPAVBIARUNFKIG","bundle":"https://pith.science/pith/JLBKPJ76DG5WLPAVBIARUNFKIG/bundle.json","state":"https://pith.science/pith/JLBKPJ76DG5WLPAVBIARUNFKIG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JLBKPJ76DG5WLPAVBIARUNFKIG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:JLBKPJ76DG5WLPAVBIARUNFKIG","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":"1150f9261a2352399584cdfe2493ef1ef48710fb21ac541d6200205d4799e9ec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-08-09T19:49:13Z","title_canon_sha256":"8fa504cecf0ff8d985027679f5b21f9e23bb705eb568cdd78dcaf9401abfd795"},"schema_version":"1.0","source":{"id":"1708.02983","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.02983","created_at":"2026-05-18T00:38:17Z"},{"alias_kind":"arxiv_version","alias_value":"1708.02983v1","created_at":"2026-05-18T00:38:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02983","created_at":"2026-05-18T00:38:17Z"},{"alias_kind":"pith_short_12","alias_value":"JLBKPJ76DG5W","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JLBKPJ76DG5WLPAV","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JLBKPJ76","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:af8fa637955ad5b048e52a19991717fbacf0b9ff4935dbe08a52665912eb80a4","target":"graph","created_at":"2026-05-18T00:38:17Z","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":"The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From a","authors_text":"Aydin Buluc, James Demmel, Yang You","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-08-09T19:49:13Z","title":"Scaling Deep Learning on GPU and Knights Landing clusters"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02983","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":"sha256:2b3820890854fef1b0bada8854b6cdd4006d4b5dad14bd00bd10d38bd48f7f8c","target":"record","created_at":"2026-05-18T00:38:17Z","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":"1150f9261a2352399584cdfe2493ef1ef48710fb21ac541d6200205d4799e9ec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2017-08-09T19:49:13Z","title_canon_sha256":"8fa504cecf0ff8d985027679f5b21f9e23bb705eb568cdd78dcaf9401abfd795"},"schema_version":"1.0","source":{"id":"1708.02983","kind":"arxiv","version":1}},"canonical_sha256":"4ac2a7a7fe19bb65bc150a011a34aa41bf85189ecaf61bc363396a3c2c5d6820","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ac2a7a7fe19bb65bc150a011a34aa41bf85189ecaf61bc363396a3c2c5d6820","first_computed_at":"2026-05-18T00:38:17.554377Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:17.554377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XRTYtpkP7wzx47/BS+3nEhVn+HYEtOYwW1F2aKKWSvV+UUxUZQfrvP/HIlHpxnXPXxIvBp/Pyac6CTuk1Z32Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:17.555111Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.02983","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b3820890854fef1b0bada8854b6cdd4006d4b5dad14bd00bd10d38bd48f7f8c","sha256:af8fa637955ad5b048e52a19991717fbacf0b9ff4935dbe08a52665912eb80a4"],"state_sha256":"0f5a09bc6168d32686681058335b1b34b2dcb7fd919fe84d192c9b4004fd1f1d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n3kbo1LE/kpzTSFbgM0Z5/IJ2eUGNfZibnMtbx0y/N01a+9YvKNjvMqx/A51ConHBq27szlvX0Ert/+1lzkwCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T06:27:06.262411Z","bundle_sha256":"fdd118350ddfd5c0a26a5aa2f5cb17db589ead1b77082a0919cb346b44a8ab95"}}