NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression (2409.05785v4)

Abstract: Large-scale scientific simulations generate massive datasets, posing challenges for storage and I/O. Traditional lossy compression struggles to advance more in balancing compression ratio, data quality, and adaptability to diverse scientific data features. While deep learning-based solutions have been explored, their common practice of relying on large models and offline training limits adaptability to dynamic data characteristics and computational efficiency. To address these challenges, we propose NeurLZ, a neural method designed to enhance lossy compression by integrating online learning, cross-field learning, and robust error regulation. Key innovations of NeurLZ include: (1) compression-time online neural learning with lightweight skipping DNN models, adapting to residual errors without costly offline pertaining, (2) the error-mitigating capability, recovering fine details from compression errors overlooked by conventional compressors, (3) $1\times$ and $2\times$ error-regulation modes, ensuring strict adherence to $1\times$ user-input error bounds strictly or relaxed 2$\times$ bounds for better overall quality, and (4) cross-field learning leveraging inter-field correlations in scientific data to improve conventional methods. Comprehensive evaluations on representative HPC datasets, e.g., Nyx, Miranda, Hurricane, against state-of-the-art compressors show NeurLZ's effectiveness. During the first five learning epochs, NeurLZ achieves an 89% bit rate reduction, with further optimization yielding up to around 94% reduction at equivalent distortion, significantly outperforming existing methods, demonstrating NeurLZ's superior performance in enhancing scientific lossy compression as a scalable and efficient solution.

• The paper presents a lightweight DNN with skipping connections that accurately predicts residuals and maintains user-defined error bounds.
• It leverages cross-field learning to capture interdependencies in scientific datasets, dramatically reducing bit rates and storage needs.
• Experimental results demonstrate up to a 90% relative reduction in bit rate, highlighting significant efficiency improvements for HPC applications.

Enhancing Lossy Compression for Scientific Data Through Neural Learning

The research paper titled "NeurLZ: On Enhancing Lossy Compression Performance based on Error-Controlled Neural Learning for Scientific Data" presents a novel approach to improving lossy compression for scientific datasets through the integration of deep learning techniques. The framework, termed NeurLZ, combines cross-field learning, error control mechanisms, and neural network-based prediction models to significantly advance the current state of lossy compression for high-performance computing (HPC) applications.

Technical Contributions

NeurLZ is structured around a two-phase system: the compression module and the reconstruction module. During the compression phase, scientific data, such as from the Nyx, Miranda, and Hurricane simulation datasets, is processed in fixed-size blocks. The process incorporates a traditional lossy compressor (e.g., SZ3 or ZFP), followed by a residual learning strategy aimed at learning the discrepancies—referred to as the residuals—between decompressed and original data. This is achieved through lightweight skipping deep neural network (DNN) models designed to minimize output storage and computational overhead while maintaining accuracy.

The research makes several noteworthy contributions:

1. Lightweight Model Design: NeurLZ introduces a lightweight DNN approach with skipping connections to facilitate high-fidelity detail retention and enhance predictive accuracy. These models are designed to minimize overhead, using approximately 3,000 parameters, making them efficient both in storage and computation, a critical consideration given the scale of scientific datasets.
2. Cross-field Learning: By leveraging patterns across different fields within the simulation data, NeurLZ's methodology captures the interdependencies that error-bounded compressors might overlook. This aspect capitalizes on shared information between fields such as temperature and velocity in complex simulations, leading to a more accurate prediction of residuals and improved compression ratios.
3. Error Control: The framework incorporates stringent error management to ensure all predictive improvements fall within user-defined error bounds. This guarantees that predictions made by the DNN models can effectively reduce the apparent error of decompressed data while maintaining the integrity required for scientific analysis.
4. Enhanced Bit Rate Efficiency: Experiments demonstrate up to a 90% relative reduction in bit rate without increasing error, compared to the best existing methods, underlining the framework's potential to contribute significantly to data management in scientific computing.

Implications and Future Directions

The paper highlights the effectiveness of NeurLZ for a diverse array of scientific datasets, showcasing significant improvements in compression efficiency and reconstructed data fidelity. Through advanced learning techniques that integrate cross-field data relationships and efficient neural model architectures, NeurLZ makes strides in overcoming traditional HPC data bottlenecks such as storage and bandwidth, which are crucial for distributed large-scale simulations and computations.

The practical implication of such techniques is significant for domains requiring massive data throughput and storage, including climate modeling, cosmological simulations, and large-scale turbulence analyses. By systematically reducing bit rate while maintaining error constraints, NeurLZ offers a scalable solution that can complement or extend existing data management frameworks in HPC environments.

The research indicates potential future developments such as refining neural networks for domain-specific optimizations and exploring the adaptability of this framework for emerging computational paradigms. Furthermore, examining other neural network architectures, like Transformers or Variational Autoencoders (VAEs), for their applicability and effectiveness in such settings could drive further advancements in this field.

Overall, NeurLZ exemplifies a robust merging of machine learning and compression algorithms, laying the groundwork for more adaptive, efficient, and accurate techniques in scientific data processing.