Abstract
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. 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.
Highlights
THE building of exascale computing systems is a huge investment both financially and in terms of scientific effort
We present the EXA2PRO framework, which enables the evaluation of applications in heterogeneous exascale computing systems
We demonstrate the use of EXA2PRO in a variety of applications, which target the transition to exascale computing systems and show how the multi-backend feature of EXA2PRO can improve the development productivity
Summary
THE building of exascale computing systems is a huge investment both financially and in terms of scientific effort. It is beneficial to enable more applications to access these systems, or at least, promote their transition towards the exascale computing direction. Towards this end, advanced programming models and environments are needed that will assist developers in addressing critical. Manuscript received 24 Feb. 2021; revised 13 July 2021; accepted 13 July 2021. Date of publication 11 Aug. 2021; date of current version 15 Oct. 2021. (Corresponding author: Lazaros Papadopoulos.) Recommended for acceptance by S.
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