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Matrix factorization (MF) is a employed by many algorithms, such as collaborating filtering, text mining and deriving hidden features of words. Out-of-core heterogeneous MF implementations are recently used to take advantage of state-of-the-art architecture and can solve problems larger than the available memory of coprocessors. Due to the data set cannot fit into the limited amount of device memory,...
Energy consumption is increasingly becoming a critical issue in HPC. There is a broad consensus that future exascale-computing will be strongly constrained by energy consumption. Heterogeneous systems usually feature higher energy efficiency than homogeneous ones since the former employ coprocessors that provide higher GFlops/Watt than CPUs. Thus, it is of great importance to better utilize the coprocessors...
Heterogeneous systems with nodes containing more than one type of computation units, e.g., central processing units (CPUs) and graphics processing units (GPUs), are becoming popular because of their low cost and high performance. In this paper, we have developed a Three-Level Parallelization Scheme (TLPS) for molecular dynamics (MD) simulation on heterogeneous systems. The scheme exploits multi-level...
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