Presentation
Invited Talk: Learning from Automatically Synthesized Compression Algorithms
DescriptionWe are generating data in larger amounts and at higher speeds than ever before. Data compression is able to mitigate the resulting storage and transmission problems, but only if the compression ratio is high enough to obtain a meaningful benefit and the throughput is sufficient to not introduce a new bottleneck. Machine learning can help by automatically synthesizing effective compression algorithms. In our work, we go a step further by employing such synthesis tools to extract valuable insights, which have enabled us to iteratively create more and more powerful compression algorithms. Ultimately, this has resulted in GPU-based compressors for scientific data that outperform the state of the art in throughput and compression ratio, both for lossless compression and for lossy compression with guaranteed point-wise error bounds.