  While both the database and high-performance computing (HPC) communities
utilize lossless compression methods to minimize floating-point data size, a
disconnect persists between them. Each community designs and assesses methods
in a domain-specific manner, making it unclear if HPC compression techniques
can benefit database applications or vice versa. With the HPC community
increasingly leaning towards in-situ analysis and visualization, more
floating-point data from scientific simulations are being stored in databases
like Key-Value Stores and queried using in-memory retrieval paradigms. This
trend underscores the urgent need for a collective study of these compression
methods' strengths and limitations, not only based on their performance in
compressing data from various domains but also on their runtime
characteristics. Our study extensively evaluates the performance of eight
CPU-based and five GPU-based compression methods developed by both communities,
using 33 real-world datasets assembled in the Floating-point Compressor
Benchmark (FCBench). Additionally, we utilize the roofline model to profile
their runtime bottlenecks. Our goal is to offer insights into these compression
methods that could assist researchers in selecting existing methods or
developing new ones for integrated database and HPC applications.
