{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Q2AVG5ZKHN5JT5HBILICTHKROU","short_pith_number":"pith:Q2AVG5ZK","schema_version":"1.0","canonical_sha256":"868153772a3b7a99f4e142d0299d51753efeb829680c35593c646c12ae248a2e","source":{"kind":"arxiv","id":"2506.11668","version":1},"attestation_state":"computed","paper":{"title":"FractalSync: Lightweight Scalable Global Synchronization of Massive Bulk Synchronous Parallel AI Accelerators","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Alessandro Nadalini, Angelo Garofalo, Davide Rossi, Francesco Conti, Riccardo Fiorani Gallotta, Victor Isachi","submitted_at":"2025-06-13T10:58:43Z","abstract_excerpt":"The slow-down of technology scaling and the emergence of Artificial Intelligence (AI) workloads have led computer architects to increasingly exploit parallelization coupled with hardware acceleration to keep pushing the performance envelope. However, this solution comes with the challenge of synchronization of processing elements (PEs) in massive heterogeneous many-core platforms. To address this challenge, we propose FractalSync, a hardware accelerated synchronization mechanism for Bulk Synchronous Parallel (BSP) systems. We integrate FractalSync in MAGIA, a scalable tile-based AI accelerator"},"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":"2506.11668","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2025-06-13T10:58:43Z","cross_cats_sorted":[],"title_canon_sha256":"fa435cdd440e9924fcd693bf81fed176e1b54acb990c7c0df138617045508042","abstract_canon_sha256":"01eee3cda2120a74cbac18028e5e87c79e38bdc192a9f3eecf36f20966117a13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:08.871342Z","signature_b64":"p6xNvPiWPPZvL5/jM2eNbcMDi3PtmSCNgvXAXzdrMufbHw+mJptvsnhLo6VuaL+le4D1STdI3RzUjRT4+mjlBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"868153772a3b7a99f4e142d0299d51753efeb829680c35593c646c12ae248a2e","last_reissued_at":"2026-07-05T11:21:08.870755Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:08.870755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FractalSync: Lightweight Scalable Global Synchronization of Massive Bulk Synchronous Parallel AI Accelerators","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Alessandro Nadalini, Angelo Garofalo, Davide Rossi, Francesco Conti, Riccardo Fiorani Gallotta, Victor Isachi","submitted_at":"2025-06-13T10:58:43Z","abstract_excerpt":"The slow-down of technology scaling and the emergence of Artificial Intelligence (AI) workloads have led computer architects to increasingly exploit parallelization coupled with hardware acceleration to keep pushing the performance envelope. However, this solution comes with the challenge of synchronization of processing elements (PEs) in massive heterogeneous many-core platforms. To address this challenge, we propose FractalSync, a hardware accelerated synchronization mechanism for Bulk Synchronous Parallel (BSP) systems. We integrate FractalSync in MAGIA, a scalable tile-based AI accelerator"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11668","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/2506.11668/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":"2506.11668","created_at":"2026-07-05T11:21:08.870834+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.11668v1","created_at":"2026-07-05T11:21:08.870834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11668","created_at":"2026-07-05T11:21:08.870834+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q2AVG5ZKHN5J","created_at":"2026-07-05T11:21:08.870834+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q2AVG5ZKHN5JT5HB","created_at":"2026-07-05T11:21:08.870834+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q2AVG5ZK","created_at":"2026-07-05T11:21:08.870834+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/Q2AVG5ZKHN5JT5HBILICTHKROU","json":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU.json","graph_json":"https://pith.science/api/pith-number/Q2AVG5ZKHN5JT5HBILICTHKROU/graph.json","events_json":"https://pith.science/api/pith-number/Q2AVG5ZKHN5JT5HBILICTHKROU/events.json","paper":"https://pith.science/paper/Q2AVG5ZK"},"agent_actions":{"view_html":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU","download_json":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU.json","view_paper":"https://pith.science/paper/Q2AVG5ZK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.11668&json=true","fetch_graph":"https://pith.science/api/pith-number/Q2AVG5ZKHN5JT5HBILICTHKROU/graph.json","fetch_events":"https://pith.science/api/pith-number/Q2AVG5ZKHN5JT5HBILICTHKROU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU/action/storage_attestation","attest_author":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU/action/author_attestation","sign_citation":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU/action/citation_signature","submit_replication":"https://pith.science/pith/Q2AVG5ZKHN5JT5HBILICTHKROU/action/replication_record"}},"created_at":"2026-07-05T11:21:08.870834+00:00","updated_at":"2026-07-05T11:21:08.870834+00:00"}