New paper slashes GPU LZ77 decode latency by 8.4x
GPU LZ77 decode bottlenecks identified and crushed in new arXiv paper
A new arXiv paper from Yakiv Shavidze dismantles long-held assumptions about GPU LZ77 decoding bottlenecks, revealing that parsing—not data copying—dominates decode latency on NVIDIA H100 GPUs, consuming 64-72% of device-resident time. Through rigorous profiling across three decoder architectures, Shavidze demonstrates that bounding back-reference chain depth (costing just 0.006% in compression ratio) impacts latency by at most 2.8%, while self-overlapping matches are periodic fills that can be parallelized, yielding 2.75-8.42x speedups in the match layer.
The paper’s most striking finding is that the last truly sequential element—a four-entry distance history—can be eliminated at the encoder stage for a mere 0.540% compression ratio cost, expanding dependency-free parse runs from 4 commands to 706. This effectively removes the final serialization bottleneck in GPU LZ77 decoding. However, the work also highlights a persistent architectural ceiling: with median match sizes of just 7 bytes against 128-byte cache lines, bus efficiency plummets to 4.4%, and coalesced writes are 39x faster than scattered writes. All reproducible claims are machine-verified, with 17 of 17 checks passing without GPU access.
- Parse operations account for 64-72% of GPU LZ77 decode time on NVIDIA H100 GPUs, not data copying as previously assumed
- Eliminating a four-entry distance history at encode time (0.54% ratio loss) increases dependency-free parse runs from 4 to 706 commands, accelerating match layers by 2.75-8.42x
- Bus efficiency for median 7-byte matches against 128-byte cache lines drops to 4.4%, with coalesced writes 39x faster than scattered writes
Why It Matters
This work redefines GPU compression bottlenecks, enabling 8x faster LZ77 decoding that could transform real-time data processing in AI pipelines and distributed systems.