{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IRALRQN7Q45TKUNA6RKNXVIAQM","short_pith_number":"pith:IRALRQN7","canonical_record":{"source":{"id":"2501.14925","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PF","submitted_at":"2025-01-24T21:31:11Z","cross_cats_sorted":[],"title_canon_sha256":"2ea296731ad57d70118e695664272063d6acf6679dd72964e791083b2a1213eb","abstract_canon_sha256":"31b75559aa73e699d79b70057401245f1e77d2b4a3b0d4cbc19f7a142af65eec"},"schema_version":"1.0"},"canonical_sha256":"4440b8c1bf873b3551a0f454dbd500831c8875edca447e15692e7cfb4cbc3dcb","source":{"kind":"arxiv","id":"2501.14925","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14925","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14925v2","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14925","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"pith_short_12","alias_value":"IRALRQN7Q45T","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"pith_short_16","alias_value":"IRALRQN7Q45TKUNA","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"pith_short_8","alias_value":"IRALRQN7","created_at":"2026-07-05T10:06:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IRALRQN7Q45TKUNA6RKNXVIAQM","target":"record","payload":{"canonical_record":{"source":{"id":"2501.14925","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PF","submitted_at":"2025-01-24T21:31:11Z","cross_cats_sorted":[],"title_canon_sha256":"2ea296731ad57d70118e695664272063d6acf6679dd72964e791083b2a1213eb","abstract_canon_sha256":"31b75559aa73e699d79b70057401245f1e77d2b4a3b0d4cbc19f7a142af65eec"},"schema_version":"1.0"},"canonical_sha256":"4440b8c1bf873b3551a0f454dbd500831c8875edca447e15692e7cfb4cbc3dcb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:33.081535Z","signature_b64":"Y7SY6wqI1bm3MRDH8bsU3aFz8YOdAITqidV4GUa1+xSDx6OStcovfGf8TsSfzRvsLTmMjxCPjn+iFTBCdBApAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4440b8c1bf873b3551a0f454dbd500831c8875edca447e15692e7cfb4cbc3dcb","last_reissued_at":"2026-07-05T10:06:33.081073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:33.081073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.14925","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":"2026-07-05T10:06:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xjh54fmlDY7DxiUUyvOCZjCVb3wUGjuQnKSNv87qn0a4RYFX686y+ioAYp6Et6PbAIstsbYv9SjFfYvCHxjPBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:47:05.759019Z"},"content_sha256":"c54f74d5374add6d03222271b2bffe4175a273526aa5746dfc6abab6c7bc477a","schema_version":"1.0","event_id":"sha256:c54f74d5374add6d03222271b2bffe4175a273526aa5746dfc6abab6c7bc477a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IRALRQN7Q45TKUNA6RKNXVIAQM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Profiling Apple Silicon Performance for ML Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.PF","authors_text":"Dahua Feng, Felix Xiaozhu Lin, Rongxiang Wang, Zhiming Xu","submitted_at":"2025-01-24T21:31:11Z","abstract_excerpt":"Apple Silicon has attracted much attention for its performance and role in machine learning (ML) training. Unlike NVIDIA GPUs, which have traditionally dominated ML training, Apple Silicon has a significant difference in memory architecture. It uses Unified Memory, which integrates CPU and GPU memory instead of separate CPU memory and GPU VRAM. However, it is difficult to tell whether Unified Memory means more performance benefits.\n  This paper investigates the performance differences by training several large language model (LLM) workloads end-to-end under different memory scenarios. The resu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14925","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.14925/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:06:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ctnBP/v6ivXAN+eRZ9z0YYjNRnIEXAfE7u+EZWms3wsFvjXBF/VZoup5ow/nC7bATk5UlX5w/oZHf07mLlcKBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T09:47:05.759911Z"},"content_sha256":"616d3d958542735217442409c3dcf8881e8d539fb1ad7b49558dae5c21a18971","schema_version":"1.0","event_id":"sha256:616d3d958542735217442409c3dcf8881e8d539fb1ad7b49558dae5c21a18971"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IRALRQN7Q45TKUNA6RKNXVIAQM/bundle.json","state_url":"https://pith.science/pith/IRALRQN7Q45TKUNA6RKNXVIAQM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IRALRQN7Q45TKUNA6RKNXVIAQM/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":"2026-08-11T09:47:05Z","links":{"resolver":"https://pith.science/pith/IRALRQN7Q45TKUNA6RKNXVIAQM","bundle":"https://pith.science/pith/IRALRQN7Q45TKUNA6RKNXVIAQM/bundle.json","state":"https://pith.science/pith/IRALRQN7Q45TKUNA6RKNXVIAQM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IRALRQN7Q45TKUNA6RKNXVIAQM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IRALRQN7Q45TKUNA6RKNXVIAQM","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":"31b75559aa73e699d79b70057401245f1e77d2b4a3b0d4cbc19f7a142af65eec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PF","submitted_at":"2025-01-24T21:31:11Z","title_canon_sha256":"2ea296731ad57d70118e695664272063d6acf6679dd72964e791083b2a1213eb"},"schema_version":"1.0","source":{"id":"2501.14925","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14925","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14925v2","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14925","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"pith_short_12","alias_value":"IRALRQN7Q45T","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"pith_short_16","alias_value":"IRALRQN7Q45TKUNA","created_at":"2026-07-05T10:06:33Z"},{"alias_kind":"pith_short_8","alias_value":"IRALRQN7","created_at":"2026-07-05T10:06:33Z"}],"graph_snapshots":[{"event_id":"sha256:616d3d958542735217442409c3dcf8881e8d539fb1ad7b49558dae5c21a18971","target":"graph","created_at":"2026-07-05T10:06:33Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.14925/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Apple Silicon has attracted much attention for its performance and role in machine learning (ML) training. Unlike NVIDIA GPUs, which have traditionally dominated ML training, Apple Silicon has a significant difference in memory architecture. It uses Unified Memory, which integrates CPU and GPU memory instead of separate CPU memory and GPU VRAM. However, it is difficult to tell whether Unified Memory means more performance benefits.\n  This paper investigates the performance differences by training several large language model (LLM) workloads end-to-end under different memory scenarios. The resu","authors_text":"Dahua Feng, Felix Xiaozhu Lin, Rongxiang Wang, Zhiming Xu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PF","submitted_at":"2025-01-24T21:31:11Z","title":"Profiling Apple Silicon Performance for ML Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14925","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:c54f74d5374add6d03222271b2bffe4175a273526aa5746dfc6abab6c7bc477a","target":"record","created_at":"2026-07-05T10:06:33Z","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":"31b75559aa73e699d79b70057401245f1e77d2b4a3b0d4cbc19f7a142af65eec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.PF","submitted_at":"2025-01-24T21:31:11Z","title_canon_sha256":"2ea296731ad57d70118e695664272063d6acf6679dd72964e791083b2a1213eb"},"schema_version":"1.0","source":{"id":"2501.14925","kind":"arxiv","version":2}},"canonical_sha256":"4440b8c1bf873b3551a0f454dbd500831c8875edca447e15692e7cfb4cbc3dcb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4440b8c1bf873b3551a0f454dbd500831c8875edca447e15692e7cfb4cbc3dcb","first_computed_at":"2026-07-05T10:06:33.081073Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:06:33.081073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y7SY6wqI1bm3MRDH8bsU3aFz8YOdAITqidV4GUa1+xSDx6OStcovfGf8TsSfzRvsLTmMjxCPjn+iFTBCdBApAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:06:33.081535Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14925","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c54f74d5374add6d03222271b2bffe4175a273526aa5746dfc6abab6c7bc477a","sha256:616d3d958542735217442409c3dcf8881e8d539fb1ad7b49558dae5c21a18971"],"state_sha256":"4844598a040fd16c01fa13e71f12c2a3b605cb491c0d645b1957fdbbbd63af72"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KZ0X9BcaWAspQeNyisS+EwT1UKEx3XclgmswtIdsnsmbFdyynflo2BTN3FwgprlIFBD6BBHURe1s3FTbFkjuDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T09:47:05.766312Z","bundle_sha256":"f74f09da4b02b780de09acb6b2afb055f262531304551381e4509db935bccd6d"}}