{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:H2RL5RJYNBPJPAWUWVPBBQ7J7X","short_pith_number":"pith:H2RL5RJY","canonical_record":{"source":{"id":"1906.07840","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-06-18T23:06:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a91525be68d3f760c365936a6bf778a7725e3b4a244a711c5676e368179114cc","abstract_canon_sha256":"86ec5d5590a2f0eb354a3f5e6f87038c921e0d8f450988a78db07c71d5f75ef5"},"schema_version":"1.0"},"canonical_sha256":"3ea2bec538685e9782d4b55e10c3e9fdfbbb196c6afc1c7074576354b3ad63ed","source":{"kind":"arxiv","id":"1906.07840","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.07840","created_at":"2026-05-17T23:42:57Z"},{"alias_kind":"arxiv_version","alias_value":"1906.07840v1","created_at":"2026-05-17T23:42:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.07840","created_at":"2026-05-17T23:42:57Z"},{"alias_kind":"pith_short_12","alias_value":"H2RL5RJYNBPJ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"H2RL5RJYNBPJPAWU","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"H2RL5RJY","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:H2RL5RJYNBPJPAWUWVPBBQ7J7X","target":"record","payload":{"canonical_record":{"source":{"id":"1906.07840","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-06-18T23:06:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a91525be68d3f760c365936a6bf778a7725e3b4a244a711c5676e368179114cc","abstract_canon_sha256":"86ec5d5590a2f0eb354a3f5e6f87038c921e0d8f450988a78db07c71d5f75ef5"},"schema_version":"1.0"},"canonical_sha256":"3ea2bec538685e9782d4b55e10c3e9fdfbbb196c6afc1c7074576354b3ad63ed","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:57.599137Z","signature_b64":"Qy4vWfwv2tLxa7b3krk5FyW8HWLATmIxYZDUrpAywWYyhKdWstPnUonhKMasnM/Vlknz3OnKLMqyLtwW7FXjCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ea2bec538685e9782d4b55e10c3e9fdfbbb196c6afc1c7074576354b3ad63ed","last_reissued_at":"2026-05-17T23:42:57.598489Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:57.598489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.07840","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:42:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cdDgJqM3yffUpkqi+7cKCVUVUUB2HPWjOKfft4P73IIJFnGVgk6D5po8jlfmh2N9qOpTjGXj+4GLCUf1N6UHCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T14:26:33.062037Z"},"content_sha256":"7ab2dbecc7d89e8932af775bb3c71d22fc01b5c60f78229db666700ec1a9ec3a","schema_version":"1.0","event_id":"sha256:7ab2dbecc7d89e8932af775bb3c71d22fc01b5c60f78229db666700ec1a9ec3a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:H2RL5RJYNBPJPAWUWVPBBQ7J7X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Aws Albarghouthi, Karu Sankaralingam, Newsha Ardalani, Urmish Thakker","submitted_at":"2019-06-18T23:06:32Z","abstract_excerpt":"Porting code from CPU to GPU is costly and time-consuming; Unless much time is invested in development and optimization, it is not obvious, a priori, how much speed-up is achievable or how much room is left for improvement. Knowing the potential speed-up a priori can be very useful: It can save hundreds of engineering hours, help programmers with prioritization and algorithm selection. We aim to address this problem using machine learning in a supervised setting, using solely the single-threaded source code of the program, without having to run or profile the code. We propose a static analysis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.07840","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":""},"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":"2026-05-17T23:42:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6nTAwTzWnNm65/lM29ljN6YM747kj97zvpP0O6wGnPJr52D6qN1ODmQ3kzhKPkw4hSxTsi9kNgOvun525yqhDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T14:26:33.062603Z"},"content_sha256":"140cc3cca92b8bb9d815b88f1f6e31495682f6b99b00b942bef8e9b514af9d4c","schema_version":"1.0","event_id":"sha256:140cc3cca92b8bb9d815b88f1f6e31495682f6b99b00b942bef8e9b514af9d4c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H2RL5RJYNBPJPAWUWVPBBQ7J7X/bundle.json","state_url":"https://pith.science/pith/H2RL5RJYNBPJPAWUWVPBBQ7J7X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H2RL5RJYNBPJPAWUWVPBBQ7J7X/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":"2026-06-04T14:26:33Z","links":{"resolver":"https://pith.science/pith/H2RL5RJYNBPJPAWUWVPBBQ7J7X","bundle":"https://pith.science/pith/H2RL5RJYNBPJPAWUWVPBBQ7J7X/bundle.json","state":"https://pith.science/pith/H2RL5RJYNBPJPAWUWVPBBQ7J7X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H2RL5RJYNBPJPAWUWVPBBQ7J7X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:H2RL5RJYNBPJPAWUWVPBBQ7J7X","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":"86ec5d5590a2f0eb354a3f5e6f87038c921e0d8f450988a78db07c71d5f75ef5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-06-18T23:06:32Z","title_canon_sha256":"a91525be68d3f760c365936a6bf778a7725e3b4a244a711c5676e368179114cc"},"schema_version":"1.0","source":{"id":"1906.07840","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.07840","created_at":"2026-05-17T23:42:57Z"},{"alias_kind":"arxiv_version","alias_value":"1906.07840v1","created_at":"2026-05-17T23:42:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.07840","created_at":"2026-05-17T23:42:57Z"},{"alias_kind":"pith_short_12","alias_value":"H2RL5RJYNBPJ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"H2RL5RJYNBPJPAWU","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"H2RL5RJY","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:140cc3cca92b8bb9d815b88f1f6e31495682f6b99b00b942bef8e9b514af9d4c","target":"graph","created_at":"2026-05-17T23:42:57Z","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":"Porting code from CPU to GPU is costly and time-consuming; Unless much time is invested in development and optimization, it is not obvious, a priori, how much speed-up is achievable or how much room is left for improvement. Knowing the potential speed-up a priori can be very useful: It can save hundreds of engineering hours, help programmers with prioritization and algorithm selection. We aim to address this problem using machine learning in a supervised setting, using solely the single-threaded source code of the program, without having to run or profile the code. We propose a static analysis","authors_text":"Aws Albarghouthi, Karu Sankaralingam, Newsha Ardalani, Urmish Thakker","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-06-18T23:06:32Z","title":"A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.07840","kind":"arxiv","version":1},"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":"sha256:7ab2dbecc7d89e8932af775bb3c71d22fc01b5c60f78229db666700ec1a9ec3a","target":"record","created_at":"2026-05-17T23:42:57Z","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":"86ec5d5590a2f0eb354a3f5e6f87038c921e0d8f450988a78db07c71d5f75ef5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-06-18T23:06:32Z","title_canon_sha256":"a91525be68d3f760c365936a6bf778a7725e3b4a244a711c5676e368179114cc"},"schema_version":"1.0","source":{"id":"1906.07840","kind":"arxiv","version":1}},"canonical_sha256":"3ea2bec538685e9782d4b55e10c3e9fdfbbb196c6afc1c7074576354b3ad63ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ea2bec538685e9782d4b55e10c3e9fdfbbb196c6afc1c7074576354b3ad63ed","first_computed_at":"2026-05-17T23:42:57.598489Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:57.598489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qy4vWfwv2tLxa7b3krk5FyW8HWLATmIxYZDUrpAywWYyhKdWstPnUonhKMasnM/Vlknz3OnKLMqyLtwW7FXjCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:57.599137Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.07840","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ab2dbecc7d89e8932af775bb3c71d22fc01b5c60f78229db666700ec1a9ec3a","sha256:140cc3cca92b8bb9d815b88f1f6e31495682f6b99b00b942bef8e9b514af9d4c"],"state_sha256":"61cc50398b757e921503a6ec8fa026da1193aa41a30851a569deb27fdcbfb59b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GsdkpWOkQCk2VucmQ+8XTdTb7dbamyoux4bercAReoEXrZvPQZLo68AH08AyTXQGlj3shWmwqLFdkV5fyrovDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T14:26:33.065509Z","bundle_sha256":"1d1573d2691adaaa34712eeda74b297022946f1eb0257a3cb2566914b28e839f"}}