The race towards Exascale computing is on, and a lot of stress is put on researchers to break the boundaries of productivity and efficiency imposed by traditional programming models. Partitioned Global Address Space (PGAS) languages are an effective alternative, and the most promising path towards sustainable programming environments for exascale machines. Languages such as UPC, Fortran, Chapel, and X10 are now more widely available than ever, thanks to increased support from vendors and open-source communities. PGAS models also take the form of meta-languages and libraries, such as Unified Parallel C++ (UPC++), Co-Array C++, OpenSHMEM, MPI-3 and Global Arrays. These have the benefit of being integrated with existing languages, simplifying the learning curve for existing programmers.
The increasing availability of PGAS compilers and support software opens up more opportunities than ever for researchers and developers to test new strategies and port applications to more demanding requirements.
The scope of the PAW workshop is to provide a forum for exhibiting case studies of PGAS programming models in the context of real-world applications as a means of better understanding practical applications of PGAS technologies. We encourage the submission of papers and talks detailing practical PGAS applications, including characterizations of scalability and performance, of expressiveness and programmability, as well as any downsides or areas for improvement in existing PGAS models. In addition to informing other application programmers about the potential that is available through PGAS programming, the workshop is designed to communicate these experiences to compiler vendors, library developers, and system architects in order to achieve broader support for PGAS programming across the community.
Novel application development using the PGAS model
Real-world examples demonstrating performance, compiler optimization, error checking, and reduced software complexity.
Performance evaluation of applications running under PGAS
Algorithmic models enabled by PGAS model
Compiler and runtime environments
Libraries using/supporting PGAS and applications
Benefits of hardware abstraction and data locality on algorithm implementation.
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