EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training
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arXiv
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| Format: | Preprint |
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2025
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| author | Jamil, Hasibul Nine, MD S Q Zulkar Kosar, Tevfik |
| author_facet | Jamil, Hasibul Nine, MD S Q Zulkar Kosar, Tevfik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11035 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training Jamil, Hasibul Nine, MD S Q Zulkar Kosar, Tevfik Distributed, Parallel, and Cluster Computing 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. |
| title | EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.11035 |