Higher-level parallelization for local and distributed asynchronous task-based programming

One of the biggest challenges on the way to exascale computing is programmability in the context of performance portability. The efficient utilization of the prospective architectures of exascale supercomputers will be challenging in many ways, very much because of a massive increase of on-node parallelism, and an increase of complexity of memory hierarchies. Parallel programming models need to be able to formulate algorithms that allow exploiting these architectural peculiarities. The recent revival of interest in the industry and wider community for the C++ language has spurred a remarkable amount of standardization proposals and technical specifications. Among those efforts is the development of seamlessly integrating various types of parallelism, such as iterative parallel execution, task-based parallelism, asynchronous execution flows, continuation style computation, and explicit fork-join control flow of independent and non-homogeneous code paths. Those proposals are the foundation of a powerful high-level abstraction that allows C++ codes to deal with an ever increasing architectural complexity in recent hardware developments. In this paper, we present the results of developing those higher level parallelization facilities in HPX, a general purpose C++ runtime system for applications of any scale. The developed higher-level parallelization APIs have been designed to overcome the limitations of today's prevalently used programming models in C++ codes. HPX exposes a uniform higher-level API which gives the application programmer syntactic and semantic equivalence of various types of on-node and off-node parallelism, all of which are well integrated into the C++ type system. We show that these higher level facilities which are fully aligned with modern C++ programming concepts, are easily extensible, fully generic, and enable highly efficient parallelization on par with or better than existing equivalent applications based on OpenMP and/or MPI.