{"ID":23475538,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.19206","arxiv_id":"2609.19206","title":"Epic: Efficient Programming Paradigm for In-Storage Computing","abstract":"In-storage computing (ISC) reduces host--storage data movement by executing computation inside computational storage devices (CSDs). For multi-stage applications, realizing these benefits requires coordinating data placement, I/O--compute overlap, and device-resident state across the workflow, yet existing interfaces lack a unified abstraction for these decisions. We present Epic, an NVMe-based ISC stack that provides this abstraction by capturing data residency and lifetime in the program: location types declare logical residency, dataflow derives lifetimes for intermediate values and operation state within an invocation, and a keep primitive extends selected state across invocations. These semantics expose the complete offloaded workflow as a located, stateful dataflow. A storage-aware compiler transforms this workflow, performs movement-aware logical mapping and fusion, and exposes I/O--compute overlap; a runtime completes the plan using execution-time information, asynchronously binding work to physical resources and managing device-resident state. Across 12 file-scanning, database, and machine learning workloads, Epic is 1.6$\\times$ faster on average than the strongest of five prior ISC systems, while achieving 4.2$\\times$ speedup on average and up to 16.1$\\times$ over the corresponding host baselines, and reducing application-side code by up to 14$\\times$ in our implementations.","short_abstract":"In-storage computing (ISC) reduces host--storage data movement by executing computation inside computational storage devices (CSDs). For multi-stage applications, realizing these benefits requires coordinating data placement, I/O--compute overlap, and device-resident state across the workflow, yet existing interfaces l...","url_abs":"https://arxiv.org/abs/2609.19206","url_pdf":"https://arxiv.org/pdf/2609.19206v1","authors":"[\"Yuyue Wang\",\"Zhenyu Zhang\",\"Glenn Reinman\",\"Huaicheng Li\"]","published":"2026-09-16T10:24:35Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.PL\"]","methods":"[]","has_code":false}