# cedar: Optimized and Unified Machine Learning Input Data Pipelines

arXiv: https://arxiv.org/abs/2401.08895 · HTML: https://mlsanity.com/paper/2401.08895/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 95 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings](https://mlsanity.com/paper/2304.01433/) (2023)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models](https://mlsanity.com/paper/2104.05158/) (2021)
- [tf.data: A Machine Learning Data Processing Framework](https://mlsanity.com/paper/2101.12127/) (2021)
- [Analyzing and Mitigating Data Stalls in DNN Training](https://mlsanity.com/paper/2007.06775/) (2020)
- [Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training](https://mlsanity.com/paper/2108.09373/) (2021)
- [RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation](https://mlsanity.com/paper/2201.10095/) (2022)
- [Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines](https://mlsanity.com/paper/2111.04131/) (2021)
- [tf.data service: A Case for Disaggregating ML Input Data Processing](https://mlsanity.com/paper/2210.14826/) (2022)
- [Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines](https://mlsanity.com/paper/2202.08679/) (2022)

## Built on by (most cited first, 6 of 6)

- [MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training](https://mlsanity.com/paper/2504.09844/) (2025)
- [A Survey of LLM × DATA](https://mlsanity.com/paper/2505.18458/) (2025)
- [The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous Execution](https://mlsanity.com/paper/2501.12407/) (2025)
- [Preparation Meets Opportunity: Enhancing Data Preprocessing for ML Training With Seneca](https://mlsanity.com/paper/2511.13724/) (2025)
- [BatchWeave: A Consistent Object-Store-Native Data Plane for Large Foundation Model Training](https://mlsanity.com/paper/2605.09994/) (2026)
- [Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines](https://mlsanity.com/paper/2610.03087/) (2026)

## Abstract (arXiv)

The input data pipeline is an essential component of each machine learning (ML) training job. It is responsible for reading massive amounts of training data, processing batches of samples using complex transformations, and loading them onto training nodes at low latency and high throughput. Performant input data systems are becoming increasingly critical, driven by skyrocketing data volumes and training throughput demands. Unfortunately, current input data systems cannot fully leverage key performance optimizations, resulting in hugely inefficient infrastructures that require significant resources - or worse - underutilize expensive accelerators. To address these demands, we present cedar, an optimized and unified programming framework for ML input data pipelines. cedar allows users to define input data pipelines using composable operators that support arbitrary ML frameworks and libraries. cedar introduces an extensible optimizer that systematically applies a complex combination of optimizations (e.g., offloading, caching, prefetching, fusion, and reordering). It orchestrates processing across a customizable set of local and distributed compute resources in order to improve processing performance and efficiency, all without user input. Across eight pipelines, cedar improves performance by up to 1.87x to 10.65x compared to state-of-the-art input data systems.