G-ALP: Rethinking Light-weight Encodings for GPUs

This paper introduces G-ALP, a GPU-optimized version of ALP, which is a recent and state-of-the-art compression scheme for floating-point. This GPU-optimization is based on two core ideas. First, all parts of the decoding process must be fully data-parallelized. In this paper, we fully data-parallelize exception patching, which typically applies to only 1% of the data. While patching has negligible performance cost on CPUs, it can become the main bottleneck on GPUs if it is not data-parallel. Second, the decoding API must minimize its register footprint, a highly scarce resource on GPUs, and hence deliver just one value-at-a-time. Our unique aim is to integrate G-ALP decoding into GPU kernels that consume data from global memory, rather than let decompression be a separate kernel. We consider these two ideas general guidelines for future GPU-optimized lightweight encodings, and a significant evolution of our new FastLanes file format, making it GPU-friendly. We extensively test G-ALP in a series of microbenchmarks and evaluate its performance on an NVIDIA V100 GPU and an NVIDIA RTX4070 Super Ti GPU, demonstrating superior performance compared to NVIDIA nvCOMP and ndzip in both decoding and filtering queries.

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