A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

2025-11-06

AI Summary

To address the high storage costs and stringent lossless fidelity requirements posed by the surge of floating-point data in IoT and high-performance computing, this paper proposes the first GPU-accelerated adaptive lossless floating-point compression framework. Our method introduces three key innovations: (1) a lightweight asynchronous pipelined architecture that implicitly overlaps CPU–GPU data transfers to hide communication overhead; (2) a theoretically guaranteed error-free floating-point-to-integer transformation algorithm; and (3) adaptive sparse bit-plane encoding, robustly handling data sparsity induced by outliers. Evaluated on 12 real-world datasets, our framework achieves an average compression ratio of 0.299—9.1% better than the best prior baseline. Compression and decompression throughput reach 10.82 GB/s and 12.32 GB/s, respectively—both 2.4× higher than the state-of-the-art methods.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

The torrential influx of floating-point data from domains like IoT and HPC necessitates high-performance lossless compression to mitigate storage costs while preserving absolute data fidelity. Leveraging GPU parallelism for this task presents significant challenges, including bottlenecks in heterogeneous data movement, complexities in executing precision-preserving conversions, and performance degradation due to anomaly-induced sparsity. To address these challenges, this paper introduces a novel GPU-based framework for floating-point adaptive lossless compression. The proposed solution employs three key innovations: a lightweight asynchronous pipeline that effectively hides I/O latency during CPU-GPU data transfer; a fast and theoretically guaranteed float-to-integer conversion method that eliminates errors inherent in floating-point arithmetic; and an adaptive sparse bit-plane encoding strategy that mitigates the sparsity caused by outliers. Extensive experiments on 12 diverse datasets demonstrate that the proposed framework significantly outperforms state-of-the-art competitors, achieving an average compression ratio of 0.299 (a 9.1% relative improvement over the best competitor), an average compression throughput of 10.82 GB/s (2.4x higher), and an average decompression throughput of 12.32 GB/s (2.4x higher).

Problem

• Developing GPU-accelerated lossless compression for floating-point data
• Addressing bottlenecks in CPU-GPU data transfer and conversion
• Overcoming performance degradation from sparse anomalous data patterns

Innovation

• Lightweight asynchronous pipeline hides I/O latency
• Fast error-free float-to-integer conversion method
• Adaptive sparse bit-plane encoding handles outliers