{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ZECYQQMMG2TEYIYES4FDDLX4HP","short_pith_number":"pith:ZECYQQMM","schema_version":"1.0","canonical_sha256":"c90588418c36a64c2304970a31aefc3bf48875e0571a47a4d5af2e0bd766b50e","source":{"kind":"arxiv","id":"1605.08325","version":1},"attestation_state":"computed","paper":{"title":"Theano-MPI: a Theano-based Distributed Training Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Fei Mao, Graham W. Taylor, He Ma","submitted_at":"2016-05-26T15:13:46Z","abstract_excerpt":"We develop a scalable and extendable training framework that can utilize GPUs across nodes in a cluster and accelerate the training of deep learning models based on data parallelism. Both synchronous and asynchronous training are implemented in our framework, where parameter exchange among GPUs is based on CUDA-aware MPI. In this report, we analyze the convergence and capability of the framework to reduce training time when scaling the synchronous training of AlexNet and GoogLeNet from 2 GPUs to 8 GPUs. In addition, we explore novel ways to reduce the communication overhead caused by exchangin"},"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":"1605.08325","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-26T15:13:46Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"113d4a535adcc68d8a2edc231451747e6b806a52252662d5f025adfc647406ba","abstract_canon_sha256":"f0266e37c4465d735438b2bec73f0b0f5c89c822e24da689b3351bc1c1771e97"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:31.887721Z","signature_b64":"C03tdfF7zOMqcvpiuGP3btqBEQff4ADt97dyPfgyUWhb//zze3nUqm0c6zd3HHgwYPp2P/HQBBRIRWEl8LwYBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c90588418c36a64c2304970a31aefc3bf48875e0571a47a4d5af2e0bd766b50e","last_reissued_at":"2026-05-18T01:13:31.887044Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:31.887044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Theano-MPI: a Theano-based Distributed Training Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"cs.LG","authors_text":"Fei Mao, Graham W. Taylor, He Ma","submitted_at":"2016-05-26T15:13:46Z","abstract_excerpt":"We develop a scalable and extendable training framework that can utilize GPUs across nodes in a cluster and accelerate the training of deep learning models based on data parallelism. Both synchronous and asynchronous training are implemented in our framework, where parameter exchange among GPUs is based on CUDA-aware MPI. In this report, we analyze the convergence and capability of the framework to reduce training time when scaling the synchronous training of AlexNet and GoogLeNet from 2 GPUs to 8 GPUs. In addition, we explore novel ways to reduce the communication overhead caused by exchangin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.08325","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1605.08325","created_at":"2026-05-18T01:13:31.887152+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.08325v1","created_at":"2026-05-18T01:13:31.887152+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.08325","created_at":"2026-05-18T01:13:31.887152+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZECYQQMMG2TE","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZECYQQMMG2TEYIYE","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZECYQQMM","created_at":"2026-05-18T12:30:53.716459+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/ZECYQQMMG2TEYIYES4FDDLX4HP","json":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP.json","graph_json":"https://pith.science/api/pith-number/ZECYQQMMG2TEYIYES4FDDLX4HP/graph.json","events_json":"https://pith.science/api/pith-number/ZECYQQMMG2TEYIYES4FDDLX4HP/events.json","paper":"https://pith.science/paper/ZECYQQMM"},"agent_actions":{"view_html":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP","download_json":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP.json","view_paper":"https://pith.science/paper/ZECYQQMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.08325&json=true","fetch_graph":"https://pith.science/api/pith-number/ZECYQQMMG2TEYIYES4FDDLX4HP/graph.json","fetch_events":"https://pith.science/api/pith-number/ZECYQQMMG2TEYIYES4FDDLX4HP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP/action/storage_attestation","attest_author":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP/action/author_attestation","sign_citation":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP/action/citation_signature","submit_replication":"https://pith.science/pith/ZECYQQMMG2TEYIYES4FDDLX4HP/action/replication_record"}},"created_at":"2026-05-18T01:13:31.887152+00:00","updated_at":"2026-05-18T01:13:31.887152+00:00"}