AI 中文总结
研究绝对偏移GPU LZ77解码吞吐量的影响因素及改进方法，通过实验发现由工作粒度决定，提出编码端增大最小匹配长度的杠杆，能同时提升压缩率和解码吞吐量。
AI 中文摘要
ACEAPEX系列工作建立了一种无损LZ77格式，其反向引用是绝对输出位置，可实现并行、压缩驻留GPU解码以及亚毫秒级区域查找。但它并未确定是什么决定了这种格式的解码吞吐量，以及如何提高它。本文回答了这两个问题。通过在NVIDIA H100上进行的受控消融实验，表明解码吞吐量不受占用率、计算、地址散射或启动并行性的影响，而是由工作粒度决定：吞吐量是平均匹配长度的函数，因为短匹配会使协作线程束的大多数线程空闲。一个合成复制内核证实，随着平均匹配长度从32字节增长到1024字节，吞吐量跨度为3.5倍（212至744GB/s）。实际数据处于低端（enwik9上的平均匹配长度为6.5，FASTQ上为10.1）。然后表明这种机制产生了一种实用的编码端杠杆：按距离类别提高最小匹配长度（从6/8/10/12到12/16/24/32）可在所有八个测试数据集上同时提高压缩率和解码吞吐量，且解码内核不变。FASTQ解码从142.6GB/s提高到178.6GB/s，压缩率提高1.8%；enwik9吞吐量提高78%。这并非权衡：两者的提升都源于一个原因，即消除短匹配，其远偏移消耗的熵比节省的更多。所有数据都是位完美的（GPU路径上使用FNV，CPU路径上使用字节比较）且可通过git验证。范围明确：数据是匹配阶段、设备驻留的；熵和主机传输不在定时器范围内；查找是读取/块级的，而非坐标级的；并且我们并未声称超过硬件带宽上限。
英文摘要
The ACEAPEX line of work established a lossless LZ77 format whose back-references are absolute output positions, giving parallel, compressed-resident GPU decode with sub-millisecond region seek. What it did not establish is what governs the decode throughput of such a format, or how to improve it. This paper answers both. Through controlled ablations on an NVIDIA H100 we show that decode throughput is governed not by occupancy, compute, address scatter, or launch parallelism, but by work granularity: throughput is a function of the average match length, because a short match leaves most lanes of a cooperating warp idle. A synthetic copy kernel confirms a 3.5x throughput span (212 to 744 GB/s) as average match length grows from 32 to 1024 bytes. Real data sit at the low end (mean match length 6.5 on enwik9, 10.1 on FASTQ). We then show that this mechanism yields a practical, encode-side lever: raising the minimum match length by distance class (6/8/10/12 to 12/16/24/32) improves both compression ratio and decode throughput simultaneously on all eight tested datasets, with no exceptions and no change to the decode kernel. FASTQ decode rises from 142.6 to 178.6 GB/s while ratio improves 1.8%; enwik9 throughput rises 78%. This is not a trade-off: both gains follow from one cause, removing short matches whose far offsets cost more entropy than they save. All figures are bit-perfect (FNV on GPU paths, byte compare on CPU paths) and git-verifiable. Scope is explicit: figures are match-phase, device-resident; entropy and host transfer are outside the timer; seek is read/block-level, not coordinate-level; and we do not claim to exceed the hardware bandwidth ceiling.
Comments4 pages, 4 tables. Fourth paper in the ACEAPEX series (see arXiv:2606.04268, 2606.18900, 2606.24531)
