Lossy compression is essential for managing massive scientific data, but per-element error bounds do not translate into bounds on downstream quantities of interest (QoIs) such as regional averages, neural network predictions, or multi-field derived quantities. We present TOPIQ, a statistical error-propagation framework that predicts QoI-level bias and uncertainty from compact compression metadata (less than 0.1% of original data). TOPIQ decomposes QoIs into primitive operators with closed-form propagation rules accounting for spatial error correlation and data-error coupling; new QoIs are supported by composition at runtime with no per-QoI derivation or retraining. Across 552 evaluations spanning 4 datasets, 3 compressors, 4 QoI families, and 8 error bounds, 93.1% of configurations achieve well-calibrated predictions. Pre-computed metadata enables post-hoc uncertainty quantification for arbitrary query regions at 56x-402x speedup over direct computation. A case study demonstrates integration into an AI-driven analysis pipeline with end-to-end confidence intervals for dynamically composed queries.
Youyuan Liu, Bo Jiang, Taolue Yang et al.· 0 citations
Large-scale particle simulations produce hundreds of millions of particles, straining storage, transfer, and interactive visualization. Existing lossy compressors such as SZ3 operate in data space and provide no guarantees on downstream visualization fidelity. We propose ParticleGS, a visualization-aware framework based on 3D Gaussian Splatting (3DGS) that learns a compact representation directly optimized for rendered image quality, combining (1) a multi-stage, multi-orbit training pipeline, (2) VizMapper, a lightweight network that adapts a single trained model to user-specified visualization parameters at inference time, and (3) spatial block training with KD-tree decomposition and global fine-tuning. On a 281-million-particle HACC cosmological simulation, our 8-block model reaches 30.03 dB PSNR at 65x compression, outperforming SZ3 by 5-8 dB at comparable ratios, and generalizes without tuning to additional HACC regions and a dark-matter-only FIRE-2 simulation. It renders at 662 FPS on a single GPU, over 2,300x faster than ParaView on the full particle data.
Bo Jiang, Youyuan Liu, Taolue Yang et al.· 0 citations
As high-performance computing shifts toward GPU-accelerated exascale systems, the exponential growth of scientific data poses severe challenges to both storage capacity and I/O bandwidth. While current GPU-based lossy compressors attempt to address this by porting CPU algorithms to the device, they rely heavily on block-wise spatial decomposition to fit GPU parallelism. This approach suffers from a fundamental locality barrier: by partitioning data into independent blocks, these methods fail to capture global correlations and fragment the unified data patterns required for effective coding, severely limiting compression ratios. In this paper, we propose TZ, a novel GPU-native error-bounded lossy compressor that breaks this ceiling by adopting global Tucker decomposition. By prioritizing global spectral energy compaction over local approximation, TZ naturally maximizes the compression potential for scientific datasets. To render this computationally intensive approach practical for high-throughput GPU workflows, we introduce a highly optimized adaptive randomized SVD engine. This design allows TZ to achieve the superior compression ratios of global spectral decomposition while maintaining competitive execution speeds. Furthermore, the global processing nature of TZ enables a unified quantization and coding scheme that eliminates block artifacts and metadata overhead. Evaluation on production-scale scientific datasets demonstrates that TZ achieves approximately 10 × higher compression ratios than state-of-the-art GPU compressors under the same error bound, while maintaining competitive, high-throughput performance.
Zhuoxun Yang, Ruoyu Li, A. Subrahmanya et al.· IEEE International Symposium...· 0 citations
Error-bounded lossy compressors have been developed for years to reduce the vast volumes of scientific data generated by high-performance computing (HPC) applications and advanced scientific instruments. While these compressors have been effective in mitigating the challenges posed by massive datasets, a significant gap remains in our understanding of the fundamental compressibility limits of scientific data–an issue that critically impacts the sustainable adoption and development of efficient lossy compression techniques in practice. Classical rate-distortion theory, established by Shannon, assumes stationary 1D sources with unconstrained coding–assumptions that do not hold for scientific datasets compressed under the tiling constraints imposed by modern parallel lossy compressors. This paper addresses this gap by developing a novel framework that characterizes compressibility limits for scientific datasets under realistic tiling constraints. The contribution is two-fold. First, we establish a tile-aware, finite-blocklength extension of rate–distortion theory that advances classical 1D asymptotic formulations into a rigorous framework for piecewise 2D Gaussian random fields. To our knowledge, this is the first framework to rigorously characterize lossy compressibility limits for scientific datasets and compressor, moving beyond classical asymptotic 1D source models. Second, we conduct a comprehensive validation of the proposed modeling framework using state-of-the-art error-bounded lossy compressors and diverse real-world HPC datasets, demonstrating that our theory accurately predicts rate-distortion trends and provides actionable insights for compressor design.
Sujata Sinha, Sheng Di, Vishwas Rao et al.· IEEE International Symposium...· 0 citations
Scientific applications generate massive amounts of data, necessitating effective data reduction techniques like error-bounded lossy compression. While state-of-the-art error-bounded lossy compressors effectively reduce storage footprint, they typically treat the compressed data as monolithic blobs, forcing analysts to decompress large volumes just to access specific data modalities. Conversely, existing storage solutions designed for flexible access lack the efficiency of modern compression. To bridge this gap between efficient compression and flexible data retrieval, we propose OPAL, a unified system that harmonizes high-performance compression with flexible, on-demand data access. Our first contribution is the OPAL universal framework, an adaptive architecture that endows generic lossy compressors with on-demand retrieval capabilities, enabling flexible and progressive access across multiple spatial regions, resolutions, and precisions. Our second contribution is OPALI, a customized compression algorithm designed natively with flexible data access in mind. By leveraging novel mechanisms such as shadow anchors, hybrid storage, and grouped bit-plane coding, OPALI eliminates the overhead inherent in generic wrappers. Extensive evaluations on real-world scientific datasets demonstrate that OPAL transforms standard compressors into flexible retrieval engines, while OPALI achieves superior compression ratios and retrieval performance, significantly accelerating scientific discovery.
Longtao Zhang, Ruoyu Li, Zhuoxun Yang et al.· IEEE International Symposium...· 1 citation