Toward Exascale Computing Systems: An Energy Efficient Massive Parallel Computational Model

The emerging Exascale supercomputing system expected till 2020 will unravel many scientific mysteries. This extreme computing system will achieve a thousand-fold increase in computing power compared to the current petascale computing system. The forthcoming system will assist system designers and development communities in navigating from traditional homogeneous to the heterogeneous systems that will be incorporated into powerful accelerated GPU devices beside traditional CPUs. For achieving ExaFlops (10^18 calculations per second) performance through the ultrascale and energy-efficient system, the current technologies are facing several challenges. Massive parallelism is one of these challenges, which requires a novel energy-efficient parallel programming (PP) model for providing the massively parallel performance. In the current study, a new parallel programming model has been proposed, which is capable of achieving massively parallel performance through coarse-grained and fine-grained parallelism over inter-node and intra-node architectural-based processing. The suggested model is a tri-level hybrid of MPI, OpenMP and CUDA that is computable over a heterogeneous system with the collaboration of traditional CPUs and energy-efficient GPU devices. Furthermore, the developed model has been demonstrated by implementing dense matrix multiplication (DMM). The proposed model is considered an initial and leading model for obtaining massively parallel performance in an Exascale computing system.

@article{Ashraf2018,
  title     = {Toward Exascale Computing Systems: An Energy Efficient Massive Parallel Computational Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {2},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Usman Ashraf and Fathy Alburaei Eassa and Aiiad Ahmad Albeshri and Abdullah Algarni},
  doi       = {10.14569/IJACSA.2018.090217},
  url       = {https://doi.org/10.14569/IJACSA.2018.090217}
}