{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:YOR7R56KQUUGTALZNLLZUG5MG3","short_pith_number":"pith:YOR7R56K","canonical_record":{"source":{"id":"2007.14152","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-07-28T12:09:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"47568946114741eba873b7282daabbc4b43ebcba9f9c1bf06bdf2e929c1df402","abstract_canon_sha256":"78dff0b53d5cb3678fd1cb5e1d8bcd50e38af63c62542ca7c6746749c90af21b"},"schema_version":"1.0"},"canonical_sha256":"c3a3f8f7ca85286981796ad79a1bac36e5a16e3994e3345019ae0eb68287aec0","source":{"kind":"arxiv","id":"2007.14152","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.14152","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"arxiv_version","alias_value":"2007.14152v2","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.14152","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"pith_short_12","alias_value":"YOR7R56KQUUG","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"YOR7R56KQUUGTALZ","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"YOR7R56K","created_at":"2026-07-05T01:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:YOR7R56KQUUGTALZNLLZUG5MG3","target":"record","payload":{"canonical_record":{"source":{"id":"2007.14152","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-07-28T12:09:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"47568946114741eba873b7282daabbc4b43ebcba9f9c1bf06bdf2e929c1df402","abstract_canon_sha256":"78dff0b53d5cb3678fd1cb5e1d8bcd50e38af63c62542ca7c6746749c90af21b"},"schema_version":"1.0"},"canonical_sha256":"c3a3f8f7ca85286981796ad79a1bac36e5a16e3994e3345019ae0eb68287aec0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:32:46.125737Z","signature_b64":"sxLZ+PTnet8FACnw5nKKhJiqnLTT46pR/6xMn3OhHg8PutsDkJs6eirrtD67YBgtwlrvLkdIQQnrr7OH4PX+Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3a3f8f7ca85286981796ad79a1bac36e5a16e3994e3345019ae0eb68287aec0","last_reissued_at":"2026-07-05T01:32:46.125387Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:32:46.125387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.14152","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":"2026-07-05T01:32:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EUXNUrA0xweezOnNW35OnnSRkkDK2hAPJaITnGkP4f1Q5TcC79dIZYbmMtTBV6B6dW6LEN2KfHyr+1uza8ZbDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T05:41:02.033961Z"},"content_sha256":"0801dcd907e23b48ca00f22d7f502ce3a7c71306a266485a34845c01da165c1d","schema_version":"1.0","event_id":"sha256:0801dcd907e23b48ca00f22d7f502ce3a7c71306a266485a34845c01da165c1d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:YOR7R56KQUUGTALZNLLZUG5MG3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DC","authors_text":"Carl Pearson, Eiman Ebrahimi, Jinjun Xiong, Mert Hidayetoglu, Rakesh Nagi, Vikram Sharma Mailthody, Wen-Mei Hwu","submitted_at":"2020-07-28T12:09:43Z","abstract_excerpt":"This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Demands for network quality have increased rapidly, pushing the size and thus the memory requirements of many neural networks beyond the capacity of available accelerators. Sparse deep neural networks (SpDNN) have shown promise for reining in the memory footprint of large neural networks. However, there is room for improvement in implementing SpDNN operations on GPUs. This work presents optimized sparse matrix multiplication kernels fused with the ReLU fun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.14152","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/2007.14152/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":"2026-07-05T01:32:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y/PtKIblgF3Cl4rtQjM1IZT/RknqJMuMbEDgGmh0TkQjnFf1lcWdnp/6ZbAv6JmYxPKrAt/zWSGniOni8YeGCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-01T05:41:02.035152Z"},"content_sha256":"3e188c5f4a2196c1b682621a2c8b7ed94cfa10202b86630a5098ab7e42371c17","schema_version":"1.0","event_id":"sha256:3e188c5f4a2196c1b682621a2c8b7ed94cfa10202b86630a5098ab7e42371c17"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YOR7R56KQUUGTALZNLLZUG5MG3/bundle.json","state_url":"https://pith.science/pith/YOR7R56KQUUGTALZNLLZUG5MG3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YOR7R56KQUUGTALZNLLZUG5MG3/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":"2026-08-01T05:41:02Z","links":{"resolver":"https://pith.science/pith/YOR7R56KQUUGTALZNLLZUG5MG3","bundle":"https://pith.science/pith/YOR7R56KQUUGTALZNLLZUG5MG3/bundle.json","state":"https://pith.science/pith/YOR7R56KQUUGTALZNLLZUG5MG3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YOR7R56KQUUGTALZNLLZUG5MG3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:YOR7R56KQUUGTALZNLLZUG5MG3","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":"78dff0b53d5cb3678fd1cb5e1d8bcd50e38af63c62542ca7c6746749c90af21b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-07-28T12:09:43Z","title_canon_sha256":"47568946114741eba873b7282daabbc4b43ebcba9f9c1bf06bdf2e929c1df402"},"schema_version":"1.0","source":{"id":"2007.14152","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.14152","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"arxiv_version","alias_value":"2007.14152v2","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.14152","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"pith_short_12","alias_value":"YOR7R56KQUUG","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"YOR7R56KQUUGTALZ","created_at":"2026-07-05T01:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"YOR7R56K","created_at":"2026-07-05T01:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:3e188c5f4a2196c1b682621a2c8b7ed94cfa10202b86630a5098ab7e42371c17","target":"graph","created_at":"2026-07-05T01:32:46Z","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/2007.14152/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Demands for network quality have increased rapidly, pushing the size and thus the memory requirements of many neural networks beyond the capacity of available accelerators. Sparse deep neural networks (SpDNN) have shown promise for reining in the memory footprint of large neural networks. However, there is room for improvement in implementing SpDNN operations on GPUs. This work presents optimized sparse matrix multiplication kernels fused with the ReLU fun","authors_text":"Carl Pearson, Eiman Ebrahimi, Jinjun Xiong, Mert Hidayetoglu, Rakesh Nagi, Vikram Sharma Mailthody, Wen-Mei Hwu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-07-28T12:09:43Z","title":"At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.14152","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":"sha256:0801dcd907e23b48ca00f22d7f502ce3a7c71306a266485a34845c01da165c1d","target":"record","created_at":"2026-07-05T01:32:46Z","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":"78dff0b53d5cb3678fd1cb5e1d8bcd50e38af63c62542ca7c6746749c90af21b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2020-07-28T12:09:43Z","title_canon_sha256":"47568946114741eba873b7282daabbc4b43ebcba9f9c1bf06bdf2e929c1df402"},"schema_version":"1.0","source":{"id":"2007.14152","kind":"arxiv","version":2}},"canonical_sha256":"c3a3f8f7ca85286981796ad79a1bac36e5a16e3994e3345019ae0eb68287aec0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c3a3f8f7ca85286981796ad79a1bac36e5a16e3994e3345019ae0eb68287aec0","first_computed_at":"2026-07-05T01:32:46.125387Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:32:46.125387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sxLZ+PTnet8FACnw5nKKhJiqnLTT46pR/6xMn3OhHg8PutsDkJs6eirrtD67YBgtwlrvLkdIQQnrr7OH4PX+Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:32:46.125737Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.14152","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0801dcd907e23b48ca00f22d7f502ce3a7c71306a266485a34845c01da165c1d","sha256:3e188c5f4a2196c1b682621a2c8b7ed94cfa10202b86630a5098ab7e42371c17"],"state_sha256":"687a3114adc539c6d13f547d3cd034c2ce6246f58bda5d24c729feb7e49dae13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0heDKUgSGX7DagcIQVL4GoZ9x5GFI9p9haFPhp41Yp5+KZZmMWEcJ81lMI4Kydh6YG+tleRwq10pCvDJZ8VDDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-01T05:41:02.075953Z","bundle_sha256":"0aaa34773b36fb2d94da9eb6d66e9eee92268f7e81c814a8ba63a2a2e9493567"}}