0000000000789951
AUTHOR
André Weißenberger
Massively Parallel ANS Decoding on GPUs
In recent years, graphics processors have enabled significant advances in the fields of big data and streamed deep learning. In order to keep control of rapidly growing amounts of data and to achieve sufficient throughput rates, compression features are a key part of many applications including popular deep learning pipelines. However, as most of the respective APIs rely on CPU-based preprocessing for decoding, data decompression frequently becomes a bottleneck in accelerated compute systems. This establishes the need for efficient GPU-based solutions for decompression. Asymmetric numeral systems (ANS) represent a modern approach to entropy coding, combining superior compression results wit…
Massively Parallel Huffman Decoding on GPUs
Data compression is a fundamental building block in a wide range of applications. Besides its intended purpose to save valuable storage on hard disks, compression can be utilized to increase the effective bandwidth to attached storage as realized by state-of-the-art file systems. In the foreseeing future, on-the-fly compression and decompression will gain utmost importance for the processing of data-intensive applications such as streamed Deep Learning tasks or Next Generation Sequencing pipelines, which establishes the need for fast parallel implementations. Huffman coding is an integral part of a number of compression methods. However, efficient parallel implementation of Huffman decompre…