Performance and Power Efficient Massive Parallel Computational Model for HPC Heterogeneous Exascale Systems

Abstract

The emerging high-performance computing Exascale supercomputing system, which is anticipated to be available in 2020, will unravel many scientific mysteries. This extraordinary processing framework will accomplish a thousand-folds increment in figuring power contrasted with the current Petascale framework. The prospective framework will help development communities and researchers in exploring from conventional homogeneous to the heterogeneous frameworks that will be joined into energy efficient GPU devices along with traditional CPUs. For accomplishing ExaFlops execution through the Ultrascale framework, the present innovations are confronting several challenges. Huge parallelism is one of these challenges, which requires a novel low power consuming parallel programming approach for attaining massive performance. This paper introduced a new parallel programming model that achieves massive parallelism by combining coarse-grained and fine-grained parallelism over inter-node and intra-node computation respectively. The proposed framework is tri-hybrid of MPI, OpenMP, and compute unified device architecture (MOC) that compute input data over heterogeneous framework. We implemented the proposed model in linear algebraic dense matrix multiplication application, and compared the quantified metrics with well-known basic linear algebra subroutine libraries such as CUDA basic linear algebra subroutines library and KAUST basic linear algebra subprograms. MOC outperformed to all implemented methods and achieved massive performance by consuming less power. The proposed MOC approach can be considered an initial and leading model to deal emerging Exascale computing systems.