Publications
Enabling data analytics workflows using node-local storage
Abstract
The convergence of high-performance computing (HPC) and Big Data is a necessity with the push towards extreme-scale computing. As HPC simulations become more complex, the analytics need to process larger amounts of data, which poses significant challenges for coupling HPC simulations with Big Data analytics. This poster presents a novel node-local approach that uses a workflow management system (WMS) to enable the coupling between the simulations and the analytics in scientific workflows by leveraging node-local non-volatile random-access memory (NVRAM).
- Date
- January 1, 1970
- Authors
- Tu Mai Anh Do, Ming Jiang, Brian Gallagher, Albert Chu, Cyrus Harrison, Karan Vahi, Ewa Deelman
- Journal
- International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Dallas, TX