{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LDAQ6UWSF23H6HYDXZKMFQ7KD5","short_pith_number":"pith:LDAQ6UWS","canonical_record":{"source":{"id":"1803.03288","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-03-08T20:03:51Z","cross_cats_sorted":["cs.LG","cs.PF"],"title_canon_sha256":"a1314a8cf5cc6199fc6096b9daa2be127c4351b5ff851423b755f00780cff1e3","abstract_canon_sha256":"7685537673dbc01341d53deb8dc0fc20b9738b1a276e5a0cc61b5c2772e8bcac"},"schema_version":"1.0"},"canonical_sha256":"58c10f52d22eb67f1f03be54c2c3ea1f633cb34e2dac6ff64f36c2a51d93046c","source":{"kind":"arxiv","id":"1803.03288","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.03288","created_at":"2026-05-18T00:04:07Z"},{"alias_kind":"arxiv_version","alias_value":"1803.03288v2","created_at":"2026-05-18T00:04:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03288","created_at":"2026-05-18T00:04:07Z"},{"alias_kind":"pith_short_12","alias_value":"LDAQ6UWSF23H","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LDAQ6UWSF23H6HYD","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LDAQ6UWS","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LDAQ6UWSF23H6HYDXZKMFQ7KD5","target":"record","payload":{"canonical_record":{"source":{"id":"1803.03288","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-03-08T20:03:51Z","cross_cats_sorted":["cs.LG","cs.PF"],"title_canon_sha256":"a1314a8cf5cc6199fc6096b9daa2be127c4351b5ff851423b755f00780cff1e3","abstract_canon_sha256":"7685537673dbc01341d53deb8dc0fc20b9738b1a276e5a0cc61b5c2772e8bcac"},"schema_version":"1.0"},"canonical_sha256":"58c10f52d22eb67f1f03be54c2c3ea1f633cb34e2dac6ff64f36c2a51d93046c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:07.871825Z","signature_b64":"G1Fmml7u6rWx2d1Ygxqa/g94OR174q30bLqFpkwrhRojrex0ABny5EZuDpOBbbLykFYiCd8//Jo97pbC+OyaDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58c10f52d22eb67f1f03be54c2c3ea1f633cb34e2dac6ff64f36c2a51d93046c","last_reissued_at":"2026-05-18T00:04:07.871117Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:07.871117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.03288","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":"2026-05-18T00:04:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ey2h+a6RGOpswHs8g1ig71+o6hcN+dpRiJmGvWkqxD6B21hBgmzxAw9zhIfGy7dBaq6kTj5SpIxc1m1Ro5onDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T23:13:28.879405Z"},"content_sha256":"ad7e4fcf5afcf4649c7673c24e8eba85f452b64d3f33ae45040c28b516c47713","schema_version":"1.0","event_id":"sha256:ad7e4fcf5afcf4649c7673c24e8eba85f452b64d3f33ae45040c28b516c47713"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LDAQ6UWSF23H6HYDXZKMFQ7KD5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TicTac: Accelerating Distributed Deep Learning with Communication Scheduling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.PF"],"primary_cat":"cs.DC","authors_text":"Roy H. Campbell, Sangeetha Abdu Jyothi, Sayed Hadi Hashemi","submitted_at":"2018-03-08T20:03:51Z","abstract_excerpt":"State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communication-heavy systems depends on the computation time, communication time and the extent of overlap of computation and communication.\n  In this work, we identify a shortcoming in systems with graph representation for computation, such as TensorFlow and PyTorch, that result in high variance in iteration time --- random order of received parameters across workers. We develop a system, TicTac, to improve the iteration time by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03288","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":""},"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":"2026-05-18T00:04:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"34iaCtmOwMIIEfKqp+ge8IewQrrFy7qHv4CarHTJtaouDOU4tc2QV2WHn7n92qjB0UCyXBBITcHSPUE9OjFcCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T23:13:28.879764Z"},"content_sha256":"79bfc787c68068f803b88e7aa8a0d9fe5241ee467915164961584b79734847fb","schema_version":"1.0","event_id":"sha256:79bfc787c68068f803b88e7aa8a0d9fe5241ee467915164961584b79734847fb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LDAQ6UWSF23H6HYDXZKMFQ7KD5/bundle.json","state_url":"https://pith.science/pith/LDAQ6UWSF23H6HYDXZKMFQ7KD5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LDAQ6UWSF23H6HYDXZKMFQ7KD5/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":"2026-06-02T23:13:28Z","links":{"resolver":"https://pith.science/pith/LDAQ6UWSF23H6HYDXZKMFQ7KD5","bundle":"https://pith.science/pith/LDAQ6UWSF23H6HYDXZKMFQ7KD5/bundle.json","state":"https://pith.science/pith/LDAQ6UWSF23H6HYDXZKMFQ7KD5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LDAQ6UWSF23H6HYDXZKMFQ7KD5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LDAQ6UWSF23H6HYDXZKMFQ7KD5","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":"7685537673dbc01341d53deb8dc0fc20b9738b1a276e5a0cc61b5c2772e8bcac","cross_cats_sorted":["cs.LG","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-03-08T20:03:51Z","title_canon_sha256":"a1314a8cf5cc6199fc6096b9daa2be127c4351b5ff851423b755f00780cff1e3"},"schema_version":"1.0","source":{"id":"1803.03288","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.03288","created_at":"2026-05-18T00:04:07Z"},{"alias_kind":"arxiv_version","alias_value":"1803.03288v2","created_at":"2026-05-18T00:04:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.03288","created_at":"2026-05-18T00:04:07Z"},{"alias_kind":"pith_short_12","alias_value":"LDAQ6UWSF23H","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LDAQ6UWSF23H6HYD","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LDAQ6UWS","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:79bfc787c68068f803b88e7aa8a0d9fe5241ee467915164961584b79734847fb","target":"graph","created_at":"2026-05-18T00:04:07Z","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":"State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communication-heavy systems depends on the computation time, communication time and the extent of overlap of computation and communication.\n  In this work, we identify a shortcoming in systems with graph representation for computation, such as TensorFlow and PyTorch, that result in high variance in iteration time --- random order of received parameters across workers. We develop a system, TicTac, to improve the iteration time by","authors_text":"Roy H. Campbell, Sangeetha Abdu Jyothi, Sayed Hadi Hashemi","cross_cats":["cs.LG","cs.PF"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-03-08T20:03:51Z","title":"TicTac: Accelerating Distributed Deep Learning with Communication Scheduling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.03288","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:ad7e4fcf5afcf4649c7673c24e8eba85f452b64d3f33ae45040c28b516c47713","target":"record","created_at":"2026-05-18T00:04:07Z","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":"7685537673dbc01341d53deb8dc0fc20b9738b1a276e5a0cc61b5c2772e8bcac","cross_cats_sorted":["cs.LG","cs.PF"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-03-08T20:03:51Z","title_canon_sha256":"a1314a8cf5cc6199fc6096b9daa2be127c4351b5ff851423b755f00780cff1e3"},"schema_version":"1.0","source":{"id":"1803.03288","kind":"arxiv","version":2}},"canonical_sha256":"58c10f52d22eb67f1f03be54c2c3ea1f633cb34e2dac6ff64f36c2a51d93046c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58c10f52d22eb67f1f03be54c2c3ea1f633cb34e2dac6ff64f36c2a51d93046c","first_computed_at":"2026-05-18T00:04:07.871117Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:04:07.871117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G1Fmml7u6rWx2d1Ygxqa/g94OR174q30bLqFpkwrhRojrex0ABny5EZuDpOBbbLykFYiCd8//Jo97pbC+OyaDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:04:07.871825Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.03288","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad7e4fcf5afcf4649c7673c24e8eba85f452b64d3f33ae45040c28b516c47713","sha256:79bfc787c68068f803b88e7aa8a0d9fe5241ee467915164961584b79734847fb"],"state_sha256":"03cd429b2dba1915fb44642eb0c382b55c615038aecfde413b58d842598d9a85"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C33j53AOpcAL20btVZWJ3A696xlTbRxiZcQGtDY+iUns4CVjuzZ395yOcXTD73Uh10E9XetibxAxv9Rhi35XBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T23:13:28.881763Z","bundle_sha256":"944a7cc1da645057b1df9327db64574dfbfd20761012005233648e85478c8c13"}}