MDHIM: a parallel key/value framework for HPC
Hugh Greenberg, John Bent, Gary Grider · 2015
The long-expected convergence of High Performance Computing and Big Data Analytics is upon us. Unfortunately, the computing environments created for each workload are not necessarily conducive for the other. In this paper, we evaluate the ability of traditional high performance computing architectures to run big data analytics. We discover and describe limitations which prevent the seamless utilization of existing big data analytics tools and software. Specifically, we evaluate the effectiveness of distributed key-value stores for manipulating large data sets across tightly coupled parallel supercomputers. Although existing distributed key-value stores have proven highly effective in cloud environments, we find their performance on HPC clusters to be degraded. Accordingly, we have built an HPC specific key-value stored called the Multi-Dimensional Hierarchical Indexing Middleware (MDHIM). Using standard big data benchmarks we find that MDHIM performance more than triples that of Cassandra on HPC systems.