{"paper":{"title":"Making Locality-aware GEMM Compatible with Page-Granularity Placement on Chiplet GPUs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Euijun Chung, Hyesoon Kim, Jae Hyung Ju","submitted_at":"2026-06-10T06:47:27Z","abstract_excerpt":"Multi-chiplet GPUs scale compute throughput and high-bandwidth memory (HBM) capacity, but their non-uniform memory system makes locality between chiplets and their data critical to the GPU's performance and energy efficiency. Locality-aware scheduling and data placement identify which data should reside near each chiplet. However, in general matrix multiplication (GEMM), locality-aware data placement often becomes incompatible with a fixed page-granularity data interleaving, since the optimal granularity for mapping data across chiplets varies widely across workloads. We propose Chiplet-Contig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11718","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/2606.11718/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"}