LAMP: Improving Compression Ratio for AMR Applications via Level Associated Mapping-Based Preconditioning

Data compression can efficiently reduce the memory and persistence storage cost, which is highly desirable in modern computing systems, such as enterprise, cloud, and High-Performance Computing (HPC) environments. However, the main challenges of existing data compressors are the insufficient compression ratio and low throughput. This paper focuses on improving the compression ratio of state-of-the-art lossy compression algorithms from the view of applications. Besides, we also use the characteristics of the applications to reduce the runtime overhead. To this end, we explore the idea with Adaptive Mesh Refinement (AMR), which is widely adopted as a computational technique to reduce the amount of computation and memory required in scientific simulations. We propose Level Associated Mapping-based Preconditioning (LAMP) to improve the storage efficiency of AMR applications. The main idea is twofold. First, we utilize the high similarities among the adjacent AMR levels to precondition the data prior to compression. Second, AMR has a unique characteristic of grid structures. We utilize grid structures to rebuild a level associated mapping table, which significantly reduces the runtime overhead of LAMP. Thanks to the optimization techniques of General Matrix Multiplication (GEMM), we further accelerate the process of rebuilding AMR hierarchy for LAMP. Besides, we also block multiple adjacent coordinates within a box and further improve cache locality. The experimental results show that the compression ratios of LAMP are improved up to 63.8% compared to directly compressing the data.