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The International Workshop on Data-Intensive Scalable Computing Systems (DISCS)
We initiated and are organizing The International Workshop on Data-Intensive Scalable Computing Systems (DISCS) from 2012 to promote data-intensive/big data computing research and stimulate the community's collective interest in tackling data-intensive/big data computing R&D challenges.
Workshop Scope and Goals
Existing high-end/high-performance computing systems are designed primarily for workloads requiring high rates of computation. However, the widening performance gap between processors and I/O systems, and trends toward higher data intensity in scientific and engineering applications, suggest there is a need to rethink HEC/HPC system architectures, programming models, runtime systems, and tools with a focus on data intensive computing. The International Workshop on Data Intensive Scalable Computing Systems (DISCS) provides a forum for researchers and other interested people in the areas of data intensive computing and high performance parallel computing to exchange ideas and discuss approaches for addressing the challenges facing Big Data or data intensive computing at large scale.
DISCS Workshop Series
The Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems
We are pleased to announce that the first Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems (PDSW-DISCS’16) will be hosted at SC16: 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.