Sunway TaihuLight system is the first supercomputer offering a peak performance over 100 PFlops, which can be utilized to parallelize Non-dominated Sorting Genetic Algorithm II (NSGA-II), a standard approach to multi-objective optimization. However, insufficient off-chip memory bandwidth and limited scratchpad memory capacity of the supercomputer hinder the performance improvement of parallellizing NSGA-II. In this article, we propose an optimized parallel NSGA-II on Sunway TaihuLight system, called swNSGA-II, by utilizing process- and thread-level parallelism of the system based on an improved island/master-slave model. To overcome the hurdles of low memory bandwidth and capacity, we propose a data sharing scheme based on register-level communication that can efficiently parallelize non-dominated sorting and crowding-distance computation of NSGA-II. Several optimization techniques including vectorization, direct memory accessing, and double buffering are also adopted to further accelerate swNSGA-II. Experiment results show that the proposed swNSGA-II can achieve a speedup of 41284 on a use case of path planning, and a speedup of 62692 on ZDT1 as compared to conventional NSGA-II.

Self-Organizing Map (SOM) is a kind of artificial neural network used in unsupervised machine learning, which is widely applied to clustering, dimension reduction and visualization for high-dimensional data, etc. There are two major versions of the training algorithm: original algorithm and batch algorithm. Compared with the original, the batch algorithm has some advantages including faster convergence and less computation, and is suitable for parallelization. However, it is still confronted with the challenge of eficiency in the case of massive data, high-dimensional data or a large-scale map. In this paper, a scalable heterogeneous parallel SOM based on the batch algorithm is proposed which combines process-level and thread-level parallelism by MPI and CUDA. To boost the parallel efficiency on GPUs and make full use of the high floating-point computing capability, we design matrix operations for the the most time-consuming steps, the computation of best match units and weights update, making the steps available for the implementation by cuBLAS. In addition, the memory optimization methods are adopted. The experiments show that the proposed heterogeneous parallel SOM is effective, efficient and scalable.