mlsanityData to 7 Oct 2026

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

arXiv 2401.05994 ↗First posted 11 Jan 2024cs.CV cs.NA math.NAcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 6 of its 41 references that are in the map. First posted on arXiv 11 Jan 2024.

Builds on

6 of 41 references.

References · 6

  1. 2018TTHRESH: Tensor Compression for Multidimensional Visual Datacited by 41
  2. 2020cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Datacited by 13
  3. 2020MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reductioncited by 8
  4. 2021COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compressioncited by 8
  5. 2018Input-Aware Auto-Tuning of Compute-Bound HPC Kernelscited by 5
  6. 2024Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysiscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 4.

8 papers.

  1. 2024Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Boundscited by 5
  2. 2024Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compressioncited by 4
  3. 2025Generative Latent Diffusion for Efficient Spatiotemporal Data Reductioncited by 2
  4. 2026ANTIC: Adaptive Neural Temporal In-situ Compressorcited by 2
  5. 2026Residual Modeling for High-Fidelity Learned Compression of Scientific Datacited by 2
  6. 2024A framework for compressing unstructured scientific data via serializationcited by 0
  7. 2026Multivariate Scientific Data Compression with Learned Cross-Variable Latent Decorrelation and Autoregressive Entropy Modelingcited by 0
  8. 2026Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Boundscited by 0