Compress Better, Compute Bigger

Summary

Have you ever experienced the frustration of not being able to analyze a dataset because it's too large to fit in memory? Or perhaps you've encountered the memory wall, where computation is hindered by slow memory access? These are common challenges in data science and high-performance computing. The developers of Blosc and Blosc2 have consistently focused on achieving compression and decompression speeds that approach or even exceed memory bandwidth limits. Moreover, with the introduction of a new compute engine in Blosc2 3.0, the guiding principle has evolved to "Compress Better, Compute Bigger." This enhancement enables computations on datasets that are over 100 times larger than the available RAM, all while maintaining high performance. Continue reading to know how to operate with datasets of 8 TB in human timeframes, using your own hardware. The Importance of Better Compression​ Data compression typically requires a trade-off between speed and compression ratio. Blosc2 allows users to fine-tune this balance. They can select from a variety of codecs and filters to maximize compression, and even introduce custom ones via its plugin system. For optimal speed, it's crucial to understand and utilize modern CPU capabilities. Multicore processing, SIMD, and cache hierarchies can significantly boost compression performance. Blosc2 leverages these features to achieve speeds close to memory bandwidth limits, and sometimes even surpassing them, particularly with contemporary CPUs. However, improved compression is only part of the solution. Rapid partial decompression is crucial when quick access to large datasets is needed. Blosc2 features n-dimensional containers that support flexible slicing, essential for non-linear data access. Leveraging two-level partitioning, Blosc2 delivers high-speed data access. Think of Blosc2 as a compressed-data version of "NumPy". Furthermore, data can reside in memory, on disk, or across a network. A significant challenge is leveraging comp...