mlsanityData to 14 Oct 2026

An Overview of the Data-Loader Landscape: Comparative Performance Analysis

arXiv 2209.13705 ↗First posted 27 Sep 2022cs.DC cs.CV cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Dataloaders, in charge of moving data from storage into GPUs while training machine learning models, might hold the key to drastically improving the performance of training jobs. Recent advances have shown promise not only by considerably decreasing training time but also by offering new features such as loading data from remote storage like S3. In this paper, we are the first to distinguish the dataloader as a separate component in the Deep Learning (DL) workflow and to outline its structure and features. Finally, we offer a comprehensive comparison of the different dataloading libraries available, their trade-offs in terms of functionality, usability, and performance and the insights derived from them.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 52 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

25 of 52 references.

References · 25

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  4. 2020Scaling Laws for Neural Language Modelscited by 6,586
  5. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  6. 2018Albumentations: fast and flexible image augmentationscited by 560
  7. 2018Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysiscited by 156
  8. 2021Scaling Deep Contrastive Learning Batch Size under Memory Limited Setupcited by 127
  9. 2019MLPerf Training Benchmarkcited by 115
  10. 2020PyTorch Distributed: Experiences on Accelerating Data Parallel Trainingcited by 90
  11. 2018Benchmark Analysis of Representative Deep Neural Network Architecturescited by 77
  12. 2018AI Benchmark: Running Deep Neural Networks on Android Smartphonescited by 75
13 more
  1. 2020Analyzing and Mitigating Data Stalls in DNN Trainingcited by 36
  2. 2018Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmarkcited by 21
  3. 2016Fathom: Reference Workloads for Modern Deep Learning Methodscited by 20
  4. 2016Benchmarking State-of-the-Art Deep Learning Software Toolscited by 17
  5. 2021Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelinescited by 16
  6. 2022Deep Lake: a Lakehouse for Deep Learningcited by 13
  7. 2022dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Trainingcited by 12
  8. 2018A Comparative Measurement Study of Deep Learning as a Service Frameworkcited by 10
  9. 2019A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learningcited by 9
  10. 2018TBD: Benchmarking and Analyzing Deep Neural Network Trainingcited by 6
  11. 2019AI Matrix: A Deep Learning Benchmark for Alibaba Data Centerscited by 4
  12. 2017BENCHIP: Benchmarking Intelligence Processorscited by 2
  13. 2020AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suitecited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniquescited by 0