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EXA2PRO: A Framework for High Development Productivity on Heterogeneous Computing Systems

Lazaros Papadopoulos, Dimitris John Soudris, Christoph Kessler, August Ernstsson, Johan Ahlqvist, Nikos Vasilas, Athanasios Papadopoulos, Panos Seferlis, Charles Prouveur, Matthieu Haefele, Samuel Paul Thibault, Athanasios Salamanis, Theodoros Ioakimidis, Dionisis D. Kehagias
National Technical University of Athens, School of Electrical and Computer Engineering, Athens, Attiki, Greec
hal-03318644, (August 13, 2021)

@article{papadopoulos2021exa2pro,

   title={EXA2PRO: A Framework for High Development Productivity on Heterogeneous Computing Systems},

   author={Papadopoulos, Lazaros and Soudris, Dimitris John and Kessler, Christoph and Ernstsson, August and Ahlqvist, Johan and Vasilas, Nikos and Papadopoulos, Athanasios and Seferlis, Panos and Prouveur, Charles and Haefele, Matthieu and others},

   journal={IEEE Transactions on Parallel and Distributed Systems},

   year={2021},

   publisher={IEEE}

}

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Programming upcoming exascale computing systems is expected to be a major challenge. New programming models are required to improve programmability, by hiding the complexity of these systems from application developers. The EXA2PRO programming framework aims at improving developers productivity for applications that target heterogeneous computing systems. It is based on advanced programming models and abstractions that encapsulate low level platform-specific optimizations and it is supported by a runtime that handles application deployment on heterogeneous nodes. It supports a wide variety of platforms and accelerators (CPU, GPU, FPGA-based Data-Flow Engines), allowing developers to efficiently exploit heterogeneous computing systems, thus enabling more HPC applications to reach exascale computing systems. The EXA2PRO framework was evaluated using four HPC applications from different domains. By applying the EXA2PRO framework, the applications were automatically deployed and evaluated on a variety of computing architectures, enabling developers to obtain performance results on accelerators, test scalability on MPI clusters and productively investigate the degree by which each application can efficiently use different types of hardware resources.
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