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arXiv · 2609.19206

Programming In-Storage Computing with Located, Stateful Dataflow

Yuyue Wang·Zhenyu Zhang·Glenn Reinman·Huaicheng Li
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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.

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Yuyue Wang, Zhenyu Zhang, Glenn Reinman, Huaicheng Li. 2026-09-18. Programming In-Storage Computing with Located, Stateful Dataflow. https://arxiv.org/abs/2609.19206

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