Scalable Compression of Massive Data Collections on HPC Systems

The exponential growth of digital data poses a significant storage challenge, straining current storage systems in terms of cost, efficiency, maintainability, and available resources. For large-scale data archiving, highly efficient data compression techniques are vital for minimizing storage overhead, communication efficiency, and optimizing data retrieval performance. This paper presents a scalable parallel workflow designed to compress vast collections of files on high-performance computing systems. Leveraging the Permute-Partition-Compress (PPC) paradigm, the proposed workflow optimizes both compression ratio and processing speed. By integrating a data clustering technique, our solution effectively addresses the challenges posed by large-scale data collections in terms of compression efficiency and scalability. Experiments were conducted on the Leonardo petascale supercomputer of CINECA (leonardo-supercomputer.cineca.eu), and processed a subset of the Software Heritage archive, consisting of about 49 million files of C++ code, totaling 1.1 TB of space. Experimental results show significant performance in both compression speedup and scalability.