5 papers
What Actually Serializes GPU LZ77 Decode: Three Decoders, Three Mechanisms, and an Encode-Time Lever That Removes the Last One
Yakiv Shavidze
The sequential part of GPU LZ77 decode is not where the field assumes it is. Across three decoder architectures on an H100 we measure that parse, not copy, holds 64-72% of device-r…
What Governs Decode Throughput in Absolute-Offset GPU LZ77? A Work-Granularity Mechanism and an Encode-Time Min-Match-Length Lever
Yakiv Shavidze
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-millisecon…
Unified Position-Invariant Random Access Through Two Compression Layers via Absolute-Offset Coordinates: A Bit-Perfect Device-Resident Proof
Yakiv Shavidze
Random access into compressed data is normally confined to a single layer. Entropy-layer methods (Recoil) seek within rANS by storing intermediate decoder states; dictionary/match-…
Compressed-Resident Genomics: Full-Pipeline Device-Resident GPU LZ77 Decode with Position-Invariant Random Access
Yakiv Shavidze
Genomic archives grow faster than decompression keeps up: the European Nucleotide Archive holds tens of petabytes of fastq.gz, and gzip is fundamentally sequential. GPU decompresso…
ACEAPEX: Parallel LZ77 Decoding via Encode-Time Absolute Offset Resolution
Yakiv Shavidze
LZ77-based codecs exhibit a fundamental sequential bottleneck in decoding: each back-reference depends on previously decompressed data, preventing multi-core scaling. We present AC…