18th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2017)
Orlando, Florida USA
June 02, 2017
Call for Papers :
The technological trends of HPC system evolution indicates increasing burden for application developers in management of the unprecedented levels of complexity in hardware and the associated performance characteristics. Many existing application codes are unlikely to perform well on future systems without major modifications or even complete rewrites. It will be important to utilize, in unison, many characteristics such as multiple levels of parallelism, many lightweight cores, complex memory hierarchies, novel I/O technology, power capping, systemwide temporal/spatial performance heterogeneity and reliability concerns. The HPC community has developed new programming models, algorithms, libraries and tools to meet these challenges in order to accommodate productive code development and effective system use. However, the application community still needs to identify the benefit through practical evaluations.
Topics of Interest
- 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.
|Paper submission due:
|| January 13,2017
|Notification of Acceptance:
|| February 24,2017
|Final camera-ready paper:
|| March 15, 2017 (TBC)
|| June 02, 2017
User Name : jerish
Posted 29-11-2016 on 09:23:43 AEDT
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