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arXiv · 2508.11035

EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training

Hasibul Jamil·MD S Q Zulkar Nine·Tevfik Kosar
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Abstract

Large-scale deep learning workloads increasingly suffer from I/O bottlenecks as datasets grow beyond local storage capacities and GPU compute outpaces network and disk latencies. While recent systems optimize data-loading time, they overlook the energy cost of I/O - a critical factor at large scale. We introduce EMLIO, an Efficient Machine Learning I/O service that jointly minimizes end-to-end data-loading latency T and I/O energy consumption E across variable-latency networked storage. EMLIO deploys a lightweight data-serving daemon on storage nodes that serializes and batches raw samples, streams them over TCP with out-of-order prefetching, and integrates seamlessly with GPU-accelerated (NVIDIA DALI) preprocessing on the client side. In exhaustive evaluations over local disk, LAN (0.05 ms & 10 ms RTT), and WAN (30 ms RTT) environments, EMLIO delivers up to 8.6X faster I/O and 10.9X lower energy use compared to state-of-the-art loaders, while maintaining constant performance and energy profiles irrespective of network distance. EMLIO's service-based architecture offers a scalable blueprint for energy-aware I/O in next-generation AI clouds.

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Hasibul Jamil, MD S Q Zulkar Nine, Tevfik Kosar. 2025-08-14. EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training. https://arxiv.org/abs/2508.11035

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