Adaptive loss‐less data compression method optimized for GPU decompression

There is no doubt that data compression is very important in computer engineering. However, most lossless data compression and decompression algorithms are very hard to parallelize, because they use dictionaries updated sequentially. The main contribution of this paper is to present a new lossless data compression method that we call adaptive loss‐less (ALL) data compression. It is designed so that the data compression ratio is moderate, but decompression can be performed very efficiently on the graphics processing unit (GPU). This makes sense for applications such as training of deep learning, in which compressed archived data are decompressed many times. To show the potentiality of ALL data compression method, we have evaluated the running time using five images and five text data and compared ALL with previously published lossless data compression methods implemented in the GPU, Gompresso, CULZSS, and LZW. The data compression ratio of ALL data compression is better than the others for eight data out of these 10 data. Also, our GPU implementation on GeForce GTX 1080 GPU for ALL decompression runs 84.0 to 231 times faster than the CPU implementation on Core i7‐4790 CPU. Further, it runs 1.22 to 23.5 times faster than Gompresso, CULZSS, and LZW running on the same GPU.