第17届IEEE国际并行与分布式科学与工程计算研讨会 The 17th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2016)

活动简介

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.

This special workshop will bring together computer scientists, applied mathematicians and researchers to present, discuss and exchange ideas, results, work in progress and experiences in the area of parallel and distributed computing for problems in science and engineering applications and inter-disciplinary applications.

征稿信息

征稿范围

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 model 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.
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.