Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computing

The paper introduces a manifold‑aware encoding strategy for general coded computing (GCC) that preserves the intrinsic low‑dimensional structure of high‑dimensional datasets. Unlike traditional coded‑computing designs that ignore data structure, this approach generates coded samples that follow the natural manifold of the data, inspired by graph‑based manifold learning. Experiments on neural network inference and high‑dimensional polynomial evaluation show that the new strategy consistently and significantly reduces mean squared recovery error under straggling compared with standard GCC.

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