Zero Overhead Modern C++ for Mapping to Any Programming Model

Towards exascale computing, today's HPC systems have become heterogeneous and diverse. Accounting for both host and accelerator, the TOP10 supercomputers in 11/2017 alone provided as much as 11 different computing architectures. On top of the hardware follow the accompanying programming models: from directive based, implicit and explicit descriptions up to task-based. Scientific code developers are facing a tough choice as commitment to a specific hardware and/or programming model narrows down potential target systems. With limited development resources but usually multi-decade long project lifetimes, maintaining multiple implementations of the same algorithms to widen platform support is unfeasible for most teams. Alpaka is a standard C++, compile-time meta-programming library providing a unified, explicit, parallel programming model. On typical MPI+X parallelized applications, Alpaka enables developers to describe shared-memory, in-node parallelism. Zero-overhead abstraction is achieved by compile-time specializing C++ templates to native backends (e.g. CUDA, OpenMP, TBB, ...). Alpaka stays with modern C++ as a standardized, widely supported language without introducing pre-processor or pragma-based annotations to the user directly. It naturally allows inlining, kernel fusion and code-reuse on a single-source programming paradigm. With such, abstractions and control within the final software stack are achievable without duplicating implementations leading to a maintainable code base even for large applications.This research is sponsored as part of the "Mephisto" project by the Federal Ministry of Education and Research within the call for tender "Grundlagenorientierte Forschung für HPC-Software im Hoch- und Höchstleistungsrechnen", under the program "IKT 2020 - Forschung für Innovationen". This research used resources of the Oak Ridge Leadership Computing Facility located in the Oak Ridge National Laboratory, which is supported by the Office of Science of the Department of Energy under Contract DE-AC05-00OR22725. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 654220