# MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring

arXiv: https://arxiv.org/abs/2401.05994 · HTML: https://mlsanity.com/paper/2401.05994/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [TTHRESH: Tensor Compression for Multidimensional Visual Data](https://mlsanity.com/paper/1806.05952/) (2018)
- [cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data](https://mlsanity.com/paper/2007.09625/) (2020)
- [MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction](https://mlsanity.com/paper/2010.05872/) (2020)
- [COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression](https://mlsanity.com/paper/2111.09562/) (2021)
- [Input-Aware Auto-Tuning of Compute-Bound HPC Kernels](https://mlsanity.com/paper/1802.05371/) (2018)
- [Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis](https://mlsanity.com/paper/2401.03317/) (2024)

## Built on by (most cited first, 8 of 8)

- [Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression](https://mlsanity.com/paper/2407.04272/) (2024)
- [Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds](https://mlsanity.com/paper/2409.05357/) (2024)
- [Generative Latent Diffusion for Efficient Spatiotemporal Data Reduction](https://mlsanity.com/paper/2507.02129/) (2025)
- [ANTIC: Adaptive Neural Temporal In-situ Compressor](https://mlsanity.com/paper/2604.09543/) (2026)
- [Residual Modeling for High-Fidelity Learned Compression of Scientific Data](https://mlsanity.com/paper/2606.05389/) (2026)
- [A framework for compressing unstructured scientific data via serialization](https://mlsanity.com/paper/2410.08059/) (2024)
- [Multivariate Scientific Data Compression with Learned Cross-Variable Latent Decorrelation and Autoregressive Entropy Modeling](https://mlsanity.com/paper/2608.30262/) (2026)
- [Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Bounds](https://mlsanity.com/paper/2609.23185/) (2026)

## Abstract (arXiv)

We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capability and precise error control, MGARD addresses a wide range of requirements, including storage reduction, high-performance I/O, and in-situ data analysis. It features a unified application programming interface (API) that seamlessly operates across diverse computing architectures. MGARD has been optimized with highly-tuned GPU kernels and efficient memory and device management mechanisms, ensuring scalable and rapid operations.