Jul 2026· IEEE International Symposium on High-Performance Parallel Distributed Computing· pp. 71-84· 0 citations· 55 references
Computer Science
TL;DR
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.
Abstract
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.
The growing scale of scientific data makes lossy compression essential for reducing data volume under controllable error. Transformation-based compressors using multilevel decomposition, such as MGARD, achieve strong compression ratios but map poorly to GPU architectures. We propose BlockMGARD, an adaptive, Region-of-Interest (ROI)-supported GPU lossy compressor, with four contributions: (1) an In-cache Block decomposition leveraging GPU on-chip memory and constant lookup tables to accelerate decomposition; (2) a hybrid hierarchy combining In-cache Block and global decomposition to balance speed and compression ratio; (3) an end-to-end pipeline with fine-grained ROI error control for feature preservation; and (4) an evaluation against state-of-the-art methods on five real-world datasets. Compared to MGARD-X, BlockMGARD achieves up to 4.2x and 9.1x higher compression and decompression throughput, and up to 8.63x higher compression ratio than uniform-tolerance baselines under ROI-aware error control. Across four GPUs, BlockMGARD achieves near-ideal linear scaling and up to 1.58x I/O cost reduction over MGARD-X.
Yan-Liang Li, Qian Gong, Qing Liu et al.· 0 citations
Error-bounded lossy compression is essential for storing and transferring the vector-field data produced by large-scale scientific simulations. Although it enforces a user-specified error bound to limit numerical distortion, it does not preserve the field's topology: small admissible perturbations can create or eliminate critical points on which downstream feature analysis depends. Existing GPU compressors achieve high throughput but are topology-agnostic, whereas the only compressor with provable critical-point preservation (cpSZ) runs on the CPU at throughput far below the data-generation rates of modern GPU-based systems. We observe that, although preserving critical points is inherently a coupled and sequential constraint, it can be reformulated into independent parallel tasks, either on a per-block basis or, speculatively, on a per-point basis. We present FaCTz, the first GPU-based error-bounded lossy compressor that guarantees critical-point preservation. FaCTz provides a block-wise mode optimized for throughput and a speculative per-point mode optimized for compression ratio. Across three vector-field datasets, FaCTz preserves every critical point while achieving throughput of up to 60 GB/s, approximately two orders of magnitude (up to approximately 640x) faster than the multithreaded CPU implementation of cpSZ. Its speculative mode further improves the compression ratio by approximately a factor of two over the throughput-oriented mode.
Mingze Xia, Yuxiao Li, Sheng Di et al.· 0 citations
HPC (High Performance Computing) applications such as numerical simulations are memory-intensive. As memory grows scarce compared to compute power, compression could alleviate hardware demands by reducing memory footprint at runtime, especially on GPU. Existing compression algorithms for scientific data are not specifically designed for GPU memory usage reduction. This work investigates the requirements of such an algorithm. We identify losslessness, end-to-end throughput, and reduced memory footprint as the key characteristics for alleviating memory requirements and improving overall resource utilization. We introduce bxCOMP, a lightweight, low-overhead, lossless GPU compression algorithm for scientific data, and describe its implementation. The proposed block-based approach outperforms state-of-the-art scientific data compression methods in decompression throughput and retains 75% of the best compression ratios on challenging real-life HPC datasets. A preprocessing phase precomputes writing positions and compressed size. Hence, no extra memory allocation is needed during compression, increasing the upper size limit of input data for identical memory resources. This also removes the need for a final copy to a tight buffer, allowing bxCOMP to match existing algorithms in end-to-end compression throughput. Optimal block size can be adjusted between iterations to optimize compression ratio throughout the simulation.
T. Hovi, François Letierce, L. Lucas· Proceedings of the Internati...· 0 citations
FitFloat is presented, a drop-in floating-point array replacement supporting user-specified precision on GPUs with the goal of reducing storage requirements of scientific applications while maximizing performance over Unified Memory.
Modern data systems compress data at rest and decompress it only when needed to preserve interconnect bandwidth. This design is often inefficient on GPU-based compute platforms because many conventional compression techniques exhibit serial data dependencies that limit GPU parallelism, leaving resources idle. Recent NVIDIA GPUs address this decoding deficiency through the Decompression Engine (DE), an on-die, fixed-function decompression accelerator for general-purpose compression formats such as Deflate, LZ4, and Snappy. Recent work has proposed string codecs that replace frequent substrings with fixed-width codes from a small, trained dictionary, making each code's lookup independent. While these lookups can run in parallel, the resulting scattered reads and short output writes still do not align well with GPU hardware, which handles contiguous memory accesses more efficiently. We present FastPair, a GPU decoder that optimizes the existing dictionary decoding process by reorganizing lookups and assembling decoded substrings for contiguous output writes. On a B300, FastPair decodes ten real-world columns 2.4 to 4.2x faster than the DE, reaching up to 1.6 TB/s.
J. Isaacs, Francesco Gargiulo, P. Boncz et al.· 0 citations
Design rules and a reproducible evaluation protocol are contributed that jointly report quality, memory, and end-to-end speed, and a foundation for automated pipeline search under realistic single-GPU constraints is provided.
Hong-Yu Yu, Yihan Shen· 0 citations
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