We are pleased to announce that the second Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems (PDSW-DISCS’17) will be hosted at SC17: The International Conference for High Performance Computing, Networking, Storage and Analysis.  The objective of this one day joint workshop is to combine two overlapping communities and to better promote and stimulate researchers’ interactions to address some of the most critical challenges for scientific data storage, management, devices, and processing infrastructure for both traditional compute intensive simulations and data-intensive high performance computing solutions.  Special attention will be given to issues in which community collaboration can be crucial for problem identification, workload capture, solution interoperability, standards with community buy--in, and shared tools.

Many scientific problem domains continue to be extremely data intensive. Traditional high performance computing (HPC) systems and the programming models for using them such as MPI were designed from a compute-centric perspective with an emphasis on achieving high floating point computation rates. But processing, memory, and storage technologies have not kept pace and there is a widening performance gap between computation and the data management infrastructure. Hence data management has become the performance bottleneck for a significant number of applications targeting HPC systems.  Concurrently, there are increasing challenges in meeting the growing demand for analyzing experimental and observational data.  In many cases, this is leading new communities to look towards HPC platforms.  In addition, the broader computing space has seen a revolution in new tools and frameworks to support Big Data analysis and machine learning.  

There is a growing need for convergence between these two worlds.  Consequently, the U.S. Congressional Office of Management and Budget has informed the U.S. Department of Energy that new machines beyond the first exascale machines must address both the traditional simulation workloads as well as data intensive applications. This coming convergence prompts integrating these two workshops into a single entity to address the common challenges.