{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:SOILEVGTL5OJ57F7DM7PYKOZEE","short_pith_number":"pith:SOILEVGT","canonical_record":{"source":{"id":"2310.04047","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-06T06:51:16Z","cross_cats_sorted":[],"title_canon_sha256":"42b19c32cf1cce07a316c48faf1735a2e350fe39014140cbcf836d4f374338bf","abstract_canon_sha256":"27dcf10748e18902be5f7950142395845f464157052e588759ea171c40e3d0ff"},"schema_version":"1.0"},"canonical_sha256":"9390b254d35f5c9efcbf1b3efc29d92137fe128072083fb4a0546afe5946ce25","source":{"kind":"arxiv","id":"2310.04047","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04047","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04047v3","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04047","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"SOILEVGTL5OJ","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"SOILEVGTL5OJ57F7","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"SOILEVGT","created_at":"2026-07-05T10:16:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:SOILEVGTL5OJ57F7DM7PYKOZEE","target":"record","payload":{"canonical_record":{"source":{"id":"2310.04047","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-06T06:51:16Z","cross_cats_sorted":[],"title_canon_sha256":"42b19c32cf1cce07a316c48faf1735a2e350fe39014140cbcf836d4f374338bf","abstract_canon_sha256":"27dcf10748e18902be5f7950142395845f464157052e588759ea171c40e3d0ff"},"schema_version":"1.0"},"canonical_sha256":"9390b254d35f5c9efcbf1b3efc29d92137fe128072083fb4a0546afe5946ce25","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:16:31.942422Z","signature_b64":"ctWJCFU7upHePOySZcPqvOePOygC4CTrWTIO+F4TkBBgyqnjBE0HxUR1Z74HIueDfX8+C+BP81Em3w6AF2HUBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9390b254d35f5c9efcbf1b3efc29d92137fe128072083fb4a0546afe5946ce25","last_reissued_at":"2026-07-05T10:16:31.941943Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:16:31.941943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.04047","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4cQPClinA7cJqF9kaNZmqak6jt1kOfuQY3P+HvK8G3ctfwSnbv5B067qzWjkcPf49ZANju5lhY9JLj1Jb2TpDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T09:31:35.078590Z"},"content_sha256":"d4eb5443b4d0b8995a0cd0e6a08926541fb15b4b04579567b5e412a4ac0349d0","schema_version":"1.0","event_id":"sha256:d4eb5443b4d0b8995a0cd0e6a08926541fb15b4b04579567b5e412a4ac0349d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:SOILEVGTL5OJ57F7DM7PYKOZEE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ali Jannesari, Ali TehraniJamsaz, Hung Phan, Le Chen, Mihai Capot\\u{a}, Nesreen K. Ahmed, Quazi Ishtiaque Mahmud, Theodore Willke","submitted_at":"2023-10-06T06:51:16Z","abstract_excerpt":"In-Context Learning (ICL) has been shown to be a powerful technique to augment the capabilities of LLMs for a diverse range of tasks. This work proposes \\ourtool, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes. We evaluate \\ourtool \\xspace{} on $12$ applications from two well-known benchmark suites of parallel codes: NAS Parallel Benchmark and Rodinia Benchmark. Our results show that \\ourtool \\xspace{} improves the state-of-the-art LLMs (e.g., GPT-4) by 19.9\\% in NAS and 6.48\\% in Rodinia benchmark in terms of CodeBERTScore"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04047","kind":"arxiv","version":3},"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/2310.04047/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":"2026-07-05T10:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ny/a69QGmjIbFppfah+Hje6k6xQ9ZjbIiloqW3jPQtc78lzPsQM8gunNk4+xmacu6MKj2Y7Ywi6b/kL+ehgjBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T09:31:35.079099Z"},"content_sha256":"1a15e545f4b7ab2fd550dc671f7c3222d5beef3fb9b0a3b836d59584b01391ac","schema_version":"1.0","event_id":"sha256:1a15e545f4b7ab2fd550dc671f7c3222d5beef3fb9b0a3b836d59584b01391ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SOILEVGTL5OJ57F7DM7PYKOZEE/bundle.json","state_url":"https://pith.science/pith/SOILEVGTL5OJ57F7DM7PYKOZEE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SOILEVGTL5OJ57F7DM7PYKOZEE/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":"2026-08-22T09:31:35Z","links":{"resolver":"https://pith.science/pith/SOILEVGTL5OJ57F7DM7PYKOZEE","bundle":"https://pith.science/pith/SOILEVGTL5OJ57F7DM7PYKOZEE/bundle.json","state":"https://pith.science/pith/SOILEVGTL5OJ57F7DM7PYKOZEE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SOILEVGTL5OJ57F7DM7PYKOZEE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SOILEVGTL5OJ57F7DM7PYKOZEE","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":"27dcf10748e18902be5f7950142395845f464157052e588759ea171c40e3d0ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-06T06:51:16Z","title_canon_sha256":"42b19c32cf1cce07a316c48faf1735a2e350fe39014140cbcf836d4f374338bf"},"schema_version":"1.0","source":{"id":"2310.04047","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04047","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04047v3","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04047","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"SOILEVGTL5OJ","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"SOILEVGTL5OJ57F7","created_at":"2026-07-05T10:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"SOILEVGT","created_at":"2026-07-05T10:16:31Z"}],"graph_snapshots":[{"event_id":"sha256:1a15e545f4b7ab2fd550dc671f7c3222d5beef3fb9b0a3b836d59584b01391ac","target":"graph","created_at":"2026-07-05T10:16:31Z","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/2310.04047/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-Context Learning (ICL) has been shown to be a powerful technique to augment the capabilities of LLMs for a diverse range of tasks. This work proposes \\ourtool, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes. We evaluate \\ourtool \\xspace{} on $12$ applications from two well-known benchmark suites of parallel codes: NAS Parallel Benchmark and Rodinia Benchmark. Our results show that \\ourtool \\xspace{} improves the state-of-the-art LLMs (e.g., GPT-4) by 19.9\\% in NAS and 6.48\\% in Rodinia benchmark in terms of CodeBERTScore","authors_text":"Ali Jannesari, Ali TehraniJamsaz, Hung Phan, Le Chen, Mihai Capot\\u{a}, Nesreen K. Ahmed, Quazi Ishtiaque Mahmud, Theodore Willke","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-06T06:51:16Z","title":"AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04047","kind":"arxiv","version":3},"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:d4eb5443b4d0b8995a0cd0e6a08926541fb15b4b04579567b5e412a4ac0349d0","target":"record","created_at":"2026-07-05T10:16:31Z","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":"27dcf10748e18902be5f7950142395845f464157052e588759ea171c40e3d0ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-06T06:51:16Z","title_canon_sha256":"42b19c32cf1cce07a316c48faf1735a2e350fe39014140cbcf836d4f374338bf"},"schema_version":"1.0","source":{"id":"2310.04047","kind":"arxiv","version":3}},"canonical_sha256":"9390b254d35f5c9efcbf1b3efc29d92137fe128072083fb4a0546afe5946ce25","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9390b254d35f5c9efcbf1b3efc29d92137fe128072083fb4a0546afe5946ce25","first_computed_at":"2026-07-05T10:16:31.941943Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:16:31.941943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ctWJCFU7upHePOySZcPqvOePOygC4CTrWTIO+F4TkBBgyqnjBE0HxUR1Z74HIueDfX8+C+BP81Em3w6AF2HUBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:16:31.942422Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.04047","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4eb5443b4d0b8995a0cd0e6a08926541fb15b4b04579567b5e412a4ac0349d0","sha256:1a15e545f4b7ab2fd550dc671f7c3222d5beef3fb9b0a3b836d59584b01391ac"],"state_sha256":"c4450afd2d07c21e619aaaec835f510772db94be6f024e8af67abb10c036c0dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L/LniVofKDeb4JGnkg4JdN0xPJNyCstE/Y1Rol/JJx0qbgkikKdhJefMvsHwS2YdxKuhRWxJaFtFp4973HwnCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T09:31:35.084079Z","bundle_sha256":"4d847fcf07472469e851a805e326c79f43e3c98addb9027d691f5e9018f80b62"}}