With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have been developed for years. None of the existing scientific floating-point lossy data compressors, however, support effective fixed-ratio lossy compression. Yet fixed-ratio lossy compression for scientific floating-point data not only compresses to the requested ratio but also respects a user-specified error bound with higher fidelity. In this paper, we present FRaZ: a generic fixed-ratio lossy compression framework respecting user-specified error constraints. The contribution is twofold. (1) We develop an efficient iterative approach to accurately determine the appropriate error settings for different lossy compressors based on target compression ratios. (2) We perform a thorough performance and accuracy evaluation for our proposed fixed-ratio compression framework with multiple state-of-the-art error-controlled lossy compressors, using several real-world scientific floating-point datasets from different domains. Experiments show that FRaZ effectively identifies the optimum error setting in the entire error setting space of any given lossy compressor. While fixed-ratio lossy compression is slower than fixed-error compression, it provides an important new lossy compression technique for users of very large scientific floating-point datasets.
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This content will become publicly available on August 12, 2025
Significantly Improving Fixed-Ratio Compression Framework for Resource-limited Applications
Scientific simulations running on HPC facilities generate massive amount of data, putting significant pressure onto supercomputers’ storage capacity and network bandwidth. To alleviate this problem, there has been a rich body of work on reducing data volumes via error-controlled lossy compression. However, fixed-ratio compression is not very well-supported, not allowing users to appropriately allocate memory/storage space or know the data transfer time over the network in advance. To address this problem, recent ratio-controlled frameworks, such as FXRZ, have incorporated methods to predict required error bound settings to reach a user-specified compression ratio. However, these approaches fail to achieve fixed-ratio compression in an accurate, efficient and scalable fashion on diverse datasets and compression algorithms.
This work proposes an efficient, scalable, ratio-controlled lossy compression framework (CAROL). At the core of CAROL are four optimization strategies that allow for improving the prediction accuracy and runtime efficiency over state-of-the-art solutions. First, CAROL uses surrogate-based compression ratio estimation to generate training data. Second, it includes a novel calibration method to improve prediction accuracy across a variety of compressors. Third, it leverages Bayesian optimization to allow for efficient training and incremental model refinement. Forth, it uses GPU acceleration to speed up prediction. We evaluate CAROL on four compression algorithms and six scientific datasets. On average, when compared to the state-of-the-art FXRZ framework, CAROL achieves 4 × speedup in setup time and 36 × speedup in inference time, while maintaining less than 1% difference in estimation accuracy.
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- Award ID(s):
- 1907863
- PAR ID:
- 10545926
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400717932
- Page Range / eLocation ID:
- 845 to 855
- Format(s):
- Medium: X
- Location:
- Gotland Sweden
- Sponsoring Org:
- National Science Foundation
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