{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:VR7Y2SFBYTT743ORRBBXVO4BVD","short_pith_number":"pith:VR7Y2SFB","canonical_record":{"source":{"id":"2311.00368","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-01T08:43:59Z","cross_cats_sorted":["cs.MS"],"title_canon_sha256":"54bb6e83bf49698f1f0f089f8299dc1fe73ab158f82d1a2e36f4956946fa1045","abstract_canon_sha256":"bbd9d2e3f5548bac703264441fe8ca6a835b1764937b7839de254e53c9353a9f"},"schema_version":"1.0"},"canonical_sha256":"ac7f8d48a1c4e7fe6dd188437abb81a8ddb8563d87c71a75f8281834294d3b82","source":{"kind":"arxiv","id":"2311.00368","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00368","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00368v1","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00368","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"pith_short_12","alias_value":"VR7Y2SFBYTT7","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"pith_short_16","alias_value":"VR7Y2SFBYTT743OR","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"pith_short_8","alias_value":"VR7Y2SFB","created_at":"2026-07-05T07:07:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:VR7Y2SFBYTT743ORRBBXVO4BVD","target":"record","payload":{"canonical_record":{"source":{"id":"2311.00368","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-01T08:43:59Z","cross_cats_sorted":["cs.MS"],"title_canon_sha256":"54bb6e83bf49698f1f0f089f8299dc1fe73ab158f82d1a2e36f4956946fa1045","abstract_canon_sha256":"bbd9d2e3f5548bac703264441fe8ca6a835b1764937b7839de254e53c9353a9f"},"schema_version":"1.0"},"canonical_sha256":"ac7f8d48a1c4e7fe6dd188437abb81a8ddb8563d87c71a75f8281834294d3b82","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:56.489397Z","signature_b64":"CfbmqzZ4sATdv4bDEeFZltJWE4jsRxPDzAbYeUAMQdhTFX33R81VnUfZVWZt7AQxCru82rqT94t9gS7PbvwnCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac7f8d48a1c4e7fe6dd188437abb81a8ddb8563d87c71a75f8281834294d3b82","last_reissued_at":"2026-07-05T07:07:56.488957Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:56.488957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.00368","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":"2026-07-05T07:07:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iBiVO/VSeZ9jcnv10VKfC+DkDI8X5i05zvt1C95C44KSfSUhOBnl0S95WembNqViAiAhMn6CShpyZeVp3e1PAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T04:29:17.368781Z"},"content_sha256":"341d86be05d0f14ca9d37d54f9673f48e6d72a9789fc63402b5256fb88dbc286","schema_version":"1.0","event_id":"sha256:341d86be05d0f14ca9d37d54f9673f48e6d72a9789fc63402b5256fb88dbc286"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:VR7Y2SFBYTT743ORRBBXVO4BVD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MS"],"primary_cat":"cs.LG","authors_text":"Christoph Bauinger, Mohammad Zubair","submitted_at":"2023-11-01T08:43:59Z","abstract_excerpt":"In this paper, we focus on three sparse matrix operations that are relevant for machine learning applications, namely, the sparse-dense matrix multiplication (SPMM), the sampled dense-dense matrix multiplication (SDDMM), and the composition of the SDDMM with SPMM, also termed as FusedMM. We develop optimized implementations for SPMM, SDDMM, and FusedMM operations utilizing Intel oneAPI's Explicit SIMD (ESIMD) SYCL extension API. In contrast to CUDA or SYCL, the ESIMD API enables the writing of explicitly vectorized kernel code. Sparse matrix algorithms implemented with the ESIMD API achieved p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00368","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/2311.00368/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":"2026-07-05T07:07:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NmcqfZdeTWLqejgwlkI6T1ij0hs+C+KWdhuVw4XOj9yuHyYK7pdKL3vYLoEEPRGfvxAntCskawMN2HXj0PiMBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T04:29:17.369147Z"},"content_sha256":"386f92af96fd3b40641c916df2f61c5038669362e840443dbf3209c95584e3fd","schema_version":"1.0","event_id":"sha256:386f92af96fd3b40641c916df2f61c5038669362e840443dbf3209c95584e3fd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VR7Y2SFBYTT743ORRBBXVO4BVD/bundle.json","state_url":"https://pith.science/pith/VR7Y2SFBYTT743ORRBBXVO4BVD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VR7Y2SFBYTT743ORRBBXVO4BVD/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":"2026-07-24T04:29:17Z","links":{"resolver":"https://pith.science/pith/VR7Y2SFBYTT743ORRBBXVO4BVD","bundle":"https://pith.science/pith/VR7Y2SFBYTT743ORRBBXVO4BVD/bundle.json","state":"https://pith.science/pith/VR7Y2SFBYTT743ORRBBXVO4BVD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VR7Y2SFBYTT743ORRBBXVO4BVD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:VR7Y2SFBYTT743ORRBBXVO4BVD","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":"bbd9d2e3f5548bac703264441fe8ca6a835b1764937b7839de254e53c9353a9f","cross_cats_sorted":["cs.MS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-01T08:43:59Z","title_canon_sha256":"54bb6e83bf49698f1f0f089f8299dc1fe73ab158f82d1a2e36f4956946fa1045"},"schema_version":"1.0","source":{"id":"2311.00368","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.00368","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"arxiv_version","alias_value":"2311.00368v1","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00368","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"pith_short_12","alias_value":"VR7Y2SFBYTT7","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"pith_short_16","alias_value":"VR7Y2SFBYTT743OR","created_at":"2026-07-05T07:07:56Z"},{"alias_kind":"pith_short_8","alias_value":"VR7Y2SFB","created_at":"2026-07-05T07:07:56Z"}],"graph_snapshots":[{"event_id":"sha256:386f92af96fd3b40641c916df2f61c5038669362e840443dbf3209c95584e3fd","target":"graph","created_at":"2026-07-05T07:07:56Z","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/2311.00368/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we focus on three sparse matrix operations that are relevant for machine learning applications, namely, the sparse-dense matrix multiplication (SPMM), the sampled dense-dense matrix multiplication (SDDMM), and the composition of the SDDMM with SPMM, also termed as FusedMM. We develop optimized implementations for SPMM, SDDMM, and FusedMM operations utilizing Intel oneAPI's Explicit SIMD (ESIMD) SYCL extension API. In contrast to CUDA or SYCL, the ESIMD API enables the writing of explicitly vectorized kernel code. Sparse matrix algorithms implemented with the ESIMD API achieved p","authors_text":"Christoph Bauinger, Mohammad Zubair","cross_cats":["cs.MS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-01T08:43:59Z","title":"Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00368","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":"sha256:341d86be05d0f14ca9d37d54f9673f48e6d72a9789fc63402b5256fb88dbc286","target":"record","created_at":"2026-07-05T07:07:56Z","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":"bbd9d2e3f5548bac703264441fe8ca6a835b1764937b7839de254e53c9353a9f","cross_cats_sorted":["cs.MS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-01T08:43:59Z","title_canon_sha256":"54bb6e83bf49698f1f0f089f8299dc1fe73ab158f82d1a2e36f4956946fa1045"},"schema_version":"1.0","source":{"id":"2311.00368","kind":"arxiv","version":1}},"canonical_sha256":"ac7f8d48a1c4e7fe6dd188437abb81a8ddb8563d87c71a75f8281834294d3b82","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ac7f8d48a1c4e7fe6dd188437abb81a8ddb8563d87c71a75f8281834294d3b82","first_computed_at":"2026-07-05T07:07:56.488957Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:07:56.488957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CfbmqzZ4sATdv4bDEeFZltJWE4jsRxPDzAbYeUAMQdhTFX33R81VnUfZVWZt7AQxCru82rqT94t9gS7PbvwnCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:07:56.489397Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.00368","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:341d86be05d0f14ca9d37d54f9673f48e6d72a9789fc63402b5256fb88dbc286","sha256:386f92af96fd3b40641c916df2f61c5038669362e840443dbf3209c95584e3fd"],"state_sha256":"92918c5c4ae02cb50e0f33d4646ee49446cfac0cf31fc8a71cf290f66c77b4ef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g8sSvWkP4UPI5+TBQIAXErKhE7tcpETx+2ANJQv3DBPXbXx0nXVthyyoQO5FWrcoEcfyrWy6BXOeMydI+GDFAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T04:29:17.371527Z","bundle_sha256":"cb0227ed157761978f044a866d982fa7f6397e959dc1175076fd5d26dc6cbf2d"}}