{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:T2A434LDER74OF7N4UNXZCWKKY","short_pith_number":"pith:T2A434LD","schema_version":"1.0","canonical_sha256":"9e81cdf163247fc717ede51b7c8aca560c6b54928f6d2834d084c587e961489b","source":{"kind":"arxiv","id":"1904.03329","version":1},"attestation_state":"computed","paper":{"title":"Load-Balanced Sparse MTTKRP on GPUs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Aravind Sukumaran-Rajam, Israt Nisa, Jiajia Li, P. Sadayappan, Richard Vuduc","submitted_at":"2019-04-06T01:12:43Z","abstract_excerpt":"Sparse matricized tensor times Khatri-Rao product (MTTKRP) is one of the most computationally expensive kernels in sparse tensor computations. This work focuses on optimizing the MTTKRP operation on GPUs, addressing both performance and storage requirements. We begin by identifying the performance bottlenecks in directly extending the state-of-the-art CSF (compressed sparse fiber) format from CPUs to GPUs. A significant challenge with GPUs compared to multicore CPUs is that of utilizing the much greater degree of parallelism in a load-balanced fashion for irregular computations like sparse MTT"},"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":"1904.03329","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2019-04-06T01:12:43Z","cross_cats_sorted":[],"title_canon_sha256":"db34bf11280abd83a8aa7bfcdecc789d929b58c94d9a7a9dab634a2191366226","abstract_canon_sha256":"4241358bccc238c51e8da00cd44f8f91612b935d9190cc19fd62cd7694461dc0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:13.824957Z","signature_b64":"9OtUd4vEeRKydBeOJwvkOQHFQqpMSZqMyvjvQfv6aOzBgU8nfCWc7WbiFHDQNpg5lZ0nXA7pSP+1dIBohmHDAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e81cdf163247fc717ede51b7c8aca560c6b54928f6d2834d084c587e961489b","last_reissued_at":"2026-05-17T23:49:13.824445Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:13.824445Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Load-Balanced Sparse MTTKRP on GPUs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Aravind Sukumaran-Rajam, Israt Nisa, Jiajia Li, P. Sadayappan, Richard Vuduc","submitted_at":"2019-04-06T01:12:43Z","abstract_excerpt":"Sparse matricized tensor times Khatri-Rao product (MTTKRP) is one of the most computationally expensive kernels in sparse tensor computations. This work focuses on optimizing the MTTKRP operation on GPUs, addressing both performance and storage requirements. We begin by identifying the performance bottlenecks in directly extending the state-of-the-art CSF (compressed sparse fiber) format from CPUs to GPUs. A significant challenge with GPUs compared to multicore CPUs is that of utilizing the much greater degree of parallelism in a load-balanced fashion for irregular computations like sparse MTT"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.03329","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.03329","created_at":"2026-05-17T23:49:13.824534+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.03329v1","created_at":"2026-05-17T23:49:13.824534+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.03329","created_at":"2026-05-17T23:49:13.824534+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2A434LDER74","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2A434LDER74OF7N","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2A434LD","created_at":"2026-05-18T12:33:27.125529+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/T2A434LDER74OF7N4UNXZCWKKY","json":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY.json","graph_json":"https://pith.science/api/pith-number/T2A434LDER74OF7N4UNXZCWKKY/graph.json","events_json":"https://pith.science/api/pith-number/T2A434LDER74OF7N4UNXZCWKKY/events.json","paper":"https://pith.science/paper/T2A434LD"},"agent_actions":{"view_html":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY","download_json":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY.json","view_paper":"https://pith.science/paper/T2A434LD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.03329&json=true","fetch_graph":"https://pith.science/api/pith-number/T2A434LDER74OF7N4UNXZCWKKY/graph.json","fetch_events":"https://pith.science/api/pith-number/T2A434LDER74OF7N4UNXZCWKKY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY/action/storage_attestation","attest_author":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY/action/author_attestation","sign_citation":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY/action/citation_signature","submit_replication":"https://pith.science/pith/T2A434LDER74OF7N4UNXZCWKKY/action/replication_record"}},"created_at":"2026-05-17T23:49:13.824534+00:00","updated_at":"2026-05-17T23:49:13.824534+00:00"}