HPC-ST 2016 - 2016 Workshop on HPC for Science and Technology

The purpose of this workshop is to present and discuss the state-of-the-art in implementing high-performance services with applications in different domains, such as environment, climate, chemistry, physics, biology, cultural heritage etc. The contributions should focus on applications needing high-performance computing, physical modeling of large-scale problems, and the development of scalable algorithms for solving large scale problems on modern parallel and distributed high-performance computing platforms, including multicore architectures, GPGPUs/GPUs, and clusters. Scalability studies of complex computing codes on HPC platforms and the tools and development environments facilitating improved scalability are also among the expected contributions.
Furthermore, during the workshop the participants will have the opportunity to discuss and share the latest research in parallel and distributed high performance computing systems applied to scientific and technical problems. The emphasis of this workshop will be on running complex realistic applications at sustained performance in production-grade HPC environments but also on the transfer of HPC expertise toward industry, particularly SMEs.
Topics
Specific topics for this workshop include, but are not limited to, the following:
+ Multicore/manycore architectures
+ GPU support for applications
+ Parallelization of compute or data-intensive tasks
+ Data handling, integration and visualization in HPC
+ Tools and programming environments supporting high performance computing
+ Scheduling in high performance computing
+ Workflow management and remote collaboration for scientific applications
+ Benchmarks and mini-apps for new parallel programming models/languages
+ Runtime support for communication optimization: data-locality management, caching, and pre-fetching
+ Management and monitoring of runtime systems in dynamic environments.
+ System level support for high performance computing
+ Performance tools using programming model abstractions
+ Fault tolerance in parallel computing
+ Scalability of infrastructures and applications
+ Energy-aware algorithms and programming
+ Big Data and HPC integrated software stack
+ Problem-solving environments for large data
+ HPC applications in/for:
? climate modelling and weather forecasting
? remote sensing data processing
? biological and health data processing
? content management systems and digital libraries