← 研踪 PaperTrailarXiv:2610.02236 · v4
Fixed-Capacity Causal Models with Parallel Recovery for GPU Lossless Compression
首次提交 2026-09-28 20:36
版本更新 2026-11-07 21:39
雷达观测 2026-11-10 11:14
摘要
GPU lossless compression requires both compact complete frames and fast checked recovery. We evaluate fixed-capacity causal byte-bank representations under a shared 512-row probability budget, select a single representation via a pre-payload complete-cost proxy charging both empirical cross-entropy and compressed model tables, and align two rANS states per 8\,KiB chunk with the causal byte banks. Independent byte banks decode concurrently, whereas intra-word conditional banks decode in two ordered phases. On ten structured imaging and electrophysiology inputs, split-bank execution lowers aggregate median encode-plus-recovery time by 13.10\% for 0.18\% additional frame bytes, and reduces complete encoding cost by 44.16\% relative to a matched native-CPU model control while supporting a standalone CUDA-free CPU receiver. In a twelve-configuration GPU comparison, Split GPX produces 5.75\% and 7.11\% smaller complete frames than Zigshuffle + nvCOMP Zstd at 35.14\% and 19.03\% lower paired cost for 64\,KiB and 8\,KiB requests. For multi-channel sample-interleaved waveforms, extending causal differencing with zero-oracle GPU stride screening (strided-residual) reduces holdout 12-lead PTB Diagnostic ECG frames by 49.89\% (from 71.47\% to 35.81\% of raw bytes, 37.20\% smaller than Zigshuffle + Zstd-1 and 46.67\% smaller than OpenZL-9) while preserving byte parity across all single-stream benchmarks.
作者备注：10 pages, 4 figures, 4 tables
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v4 · 2026-11-07 21:39 · 最新观测版本
Fixed-Capacity Causal Models with Parallel Recovery for GPU Lossless Compression
GPU lossless compression requires both compact complete frames and fast checked recovery. We evaluate fixed-capacity causal byte-bank representations under a shared 512-row probability budget, select a single representation via a pre-payload complete-cost proxy charging both empirical cross-entropy and compressed model tables, and align two rANS states per 8\,KiB chunk with the causal byte banks. Independent byte banks decode concurrently, whereas intra-word conditional banks decode in two ordered phases. On ten structured imaging and electrophysiology inputs, split-bank execution lowers aggregate median encode-plus-recovery time by 13.10\% for 0.18\% additional frame bytes, and reduces complete encoding cost by 44.16\% relative to a matched native-CPU model control while supporting a standalone CUDA-free CPU receiver. In a twelve-configuration GPU comparison, Split GPX produces 5.75\% and 7.11\% smaller complete frames than Zigshuffle + nvCOMP Zstd at 35.14\% and 19.03\% lower paired cost for 64\,KiB and 8\,KiB requests. For multi-channel sample-interleaved waveforms, extending causal differencing with zero-oracle GPU stride screening (strided-residual) reduces holdout 12-lead PTB Diagnostic ECG frames by 49.89\% (from 71.47\% to 35.81\% of raw bytes, 37.20\% smaller than Zigshuffle + Zstd-1 and 46.67\% smaller than OpenZL-9) while preserving byte parity across all single-stream benchmarks.
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