Multilevel Active Storage for big data applications in high performance computing
Chao Chen, Michael Lang, Yong Chen · 2013
Given the growing importance of supporting dataintensive sciences and big data applications, an effective HPC I/O solution has become a key issue and has attracted intensive attention in recent years. Active storage has been shown effective in reducing data movement and network traffic as a potential new I/O solution. Existing prototypes and systems, however, are primarily designed for read-intensive applications. In addition, they generally assume that offloaded processing kernels have small computational demands, which makes this solution a poor fit for data-intensive operations that have significant computational demands, including write-intensive operations. In this research, we propose a new Multilevel Active Storage (MAS) solution. The new MAS design can support and handle both read- and write-intensive operations, as well as complex operations that have considerable computational demands. Experimental tests have been carried out and confirmed that the MAS approach is feasible and outperformed existing approaches. The new multilevel active storage design has a potential to deliver a high performance I/O solution for big data applications in HPC.