Presentation
GPUFastqLZ: An Ultra Fast Compression Methodology for Fastq Sequence Data on GPUs
DescriptionWe present gpuFastqLZ, an ultra-fast compression methodology for FASTQ sequence data on GPUs. Leveraging the high parallelism capabilities of GPUs, gpuFastqLZ incorporates several optimizations, including a fast algorithm for field separation, a 2-bit encoding scheme for base fields, and the implementation of Illumina binning and GPULZ compression algorithms.
We evaluate gpuFastqLZ on three datasets, across 324 hyperparameter settings, which shows that gpuFastqLZ outperforms existing compressors, achieving up to a 1300x speedup in compression throughput and a 1.1x improvement in compression ratio compared to GZIP and exceeds the state-of-the-art FASTQ compressor GENOZIP by up to 18X throughput.
We evaluate gpuFastqLZ on three datasets, across 324 hyperparameter settings, which shows that gpuFastqLZ outperforms existing compressors, achieving up to a 1300x speedup in compression throughput and a 1.1x improvement in compression ratio compared to GZIP and exceeds the state-of-the-art FASTQ compressor GENOZIP by up to 18X throughput.