{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:APWMW2VRHPREGDT56UYQ5MIUJ5","short_pith_number":"pith:APWMW2VR","schema_version":"1.0","canonical_sha256":"03eccb6ab13be2430e7df5310eb1144f53e7625bc750a9be8a82e25b883d45c0","source":{"kind":"arxiv","id":"2606.07019","version":1},"attestation_state":"computed","paper":{"title":"PCCL: Process Group-Aware Scalable and Generic Collective Algorithm Synthesizer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Kartik Lakhotia, Madhu Kumar, Sudarshan Srinivasan, Tushar Krishna, William Won","submitted_at":"2026-06-05T08:08:56Z","abstract_excerpt":"Distributed machine learning has become increasingly important due to the massive scale of large-scale generative models. Both model parameters and data are distributed across many compute devices, which requires frequent collective communications to synchronize activations and parameter updates. Such collective communications have become a major bottleneck. While the performance of the collective algorithm depends on the physical network topology, the baseline collective algorithms in collective communication libraries are largely topology-agnostic. Collective algorithm synthesizers address t"},"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":"2606.07019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2026-06-05T08:08:56Z","cross_cats_sorted":[],"title_canon_sha256":"c8f0fc35f04b88dd4563a372ea53508e7cfda7d020429a8954fdf3be2a092d73","abstract_canon_sha256":"a20000fd55c02a373364400992a4f37f9b7e40253a376f09104792c67f8a3b27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:41.605582Z","signature_b64":"oJf0n6KmfBr6H/IA35hqJCie49LQqQ7D+7z9OaeRWkaci5p2+AWJzw7hKULQQHfNhCit5hR7+i//a/BiFGA9Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03eccb6ab13be2430e7df5310eb1144f53e7625bc750a9be8a82e25b883d45c0","last_reissued_at":"2026-06-08T01:04:41.604669Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:41.604669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PCCL: Process Group-Aware Scalable and Generic Collective Algorithm Synthesizer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Kartik Lakhotia, Madhu Kumar, Sudarshan Srinivasan, Tushar Krishna, William Won","submitted_at":"2026-06-05T08:08:56Z","abstract_excerpt":"Distributed machine learning has become increasingly important due to the massive scale of large-scale generative models. Both model parameters and data are distributed across many compute devices, which requires frequent collective communications to synchronize activations and parameter updates. Such collective communications have become a major bottleneck. While the performance of the collective algorithm depends on the physical network topology, the baseline collective algorithms in collective communication libraries are largely topology-agnostic. Collective algorithm synthesizers address t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07019","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.07019/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.07019","created_at":"2026-06-08T01:04:41.604811+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.07019v1","created_at":"2026-06-08T01:04:41.604811+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07019","created_at":"2026-06-08T01:04:41.604811+00:00"},{"alias_kind":"pith_short_12","alias_value":"APWMW2VRHPRE","created_at":"2026-06-08T01:04:41.604811+00:00"},{"alias_kind":"pith_short_16","alias_value":"APWMW2VRHPREGDT5","created_at":"2026-06-08T01:04:41.604811+00:00"},{"alias_kind":"pith_short_8","alias_value":"APWMW2VR","created_at":"2026-06-08T01:04:41.604811+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/APWMW2VRHPREGDT56UYQ5MIUJ5","json":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5.json","graph_json":"https://pith.science/api/pith-number/APWMW2VRHPREGDT56UYQ5MIUJ5/graph.json","events_json":"https://pith.science/api/pith-number/APWMW2VRHPREGDT56UYQ5MIUJ5/events.json","paper":"https://pith.science/paper/APWMW2VR"},"agent_actions":{"view_html":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5","download_json":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5.json","view_paper":"https://pith.science/paper/APWMW2VR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.07019&json=true","fetch_graph":"https://pith.science/api/pith-number/APWMW2VRHPREGDT56UYQ5MIUJ5/graph.json","fetch_events":"https://pith.science/api/pith-number/APWMW2VRHPREGDT56UYQ5MIUJ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5/action/storage_attestation","attest_author":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5/action/author_attestation","sign_citation":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5/action/citation_signature","submit_replication":"https://pith.science/pith/APWMW2VRHPREGDT56UYQ5MIUJ5/action/replication_record"}},"created_at":"2026-06-08T01:04:41.604811+00:00","updated_at":"2026-06-08T01:04:41.604811+00:00"}