The field of high performance computing has been prominent since the 1940s, and has become increasingly significant as recent advances in electronic and integrated circuit technologies have made it more widely accessible. The hardware is becoming faster, less expensive and more cost effective, which will result in a proliferation in the application of parallel and distributed systems. Scientific and engineering application domains play a key role in shaping future research and development activities in academia and industry, especially when the solution of large and complex problems must cope with tight timing constraints.
Thus, the focus of this workshop is on methodologies and experiences of scientific and engineering applications and algorithms to achieve sustainable code development for better productivity, application performance and reliability. In particular, we will focus on the following topics in parallel and distributed scientific and engineering applications, but not limited to:
Code modernization methodologies and experiences for adapting the changes in future computing systems such as porting of legacy simulation code and libraries/tools to facilitate code refactoring and porting.
Application and algorithm development of various parallel and distributed programming models/framework such as CAF, UPC, Chapel, X10, Charm++, HPX, Uintah, Legion, and/or the interoperation of multiple models within single applications (e.g. MPI+X where X is OpenMP, OpenCL, CUDA etc). We appreciate the experiences of early adopters of new programming models and platforms.
Experience in new tools and libraries for effective application development, including performance tools, application development frameworks, Domain Specific Languages (DSLs), etc.
Tools and techniques for improving application reliability and resilience. This includes both performance and correctness issues, with the latter arising from adverse operating conditions (e.g. low power) or very large system scales.
Use cases of enterprise distributed computing technology (such as MapReduce, Data Analytics and Machine-learning tools) in scientific and engineering applications.
Large-scale parallel and distributed algorithms supporting science and engineering applications.
Methodologies and experiences in developing large-scale applications.
Jun 02
2017
Conference Date
Draft paper submission deadline
Draft Paper Acceptance Notification
Final Paper Deadline
Registration deadline
2016-05-27 United States Chicago, Illinois, USA
The 17th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2016)
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