# Clairvoyant Prefetching for Distributed Machine Learning I/O

Source: [Clairvoyant Prefetching for Distributed Machine Learning I/O](<https://www.sota2.com/research/paper/68fa6a1e1e209131cea260da>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:16:37.291Z

Authors: Nikoli Dryden, Roman B\\"ohringer, Tal Ben-Nun, Torsten Hoefler

Published: 2021-01-21T17:21:42.000Z

## Abstract

I/O is emerging as a major bottleneck for machine learning training, especially in distributed environments. Indeed, at large scale, I/O takes as much as 85% of training time. Addressing this I/O bottleneck necessitates careful optimization, as optimal data ingestion pipelines differ between systems, and require a delicate balance between access to local storage, external filesystems, and remote nodes. We introduce NoPFS, a machine learning I/O middleware, which provides a scalable, flexible, and easy-to-use solution to the I/O bottleneck. NoPFS uses clairvoyance: Given the seed generating the random access pattern for training with SGD, it can exactly predict when and where a sample will be accessed. We combine this with an analysis of access patterns and a performance model to provide distributed caching policies that adapt to different datasets and storage hierarchies. NoPFS reduces I/O times and improves end-to-end training by up to 5.4x on the ImageNet-1k, ImageNet-22k, and CosmoFlow datasets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.08734>)

[PDF](<https://arxiv.org/pdf/2101.08734.pdf>)