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

TL;DR

EMLIO is a scalable I/O system that significantly reduces latency and energy consumption for large-scale AI training by optimizing data loading over various network environments.

Contribution

We introduce EMLIO, a novel I/O service that minimizes both latency and energy use in distributed AI workloads, addressing a critical gap in current data-loading systems.

Findings

Up to 8.6X faster I/O performance

10.9X reduction in energy consumption

Consistent performance across local, LAN, and WAN environments

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.