Jun Wang

TL;DR: Simulation results show the proposed quantum immune algorithm is able to find a much better spread of solutions and has better convergence near the true Pareto-optimal front compared to the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II).

TL;DR: Numerical results show that the proposed kernels of GPUMICPCGA outperform the corresponding ones presented in CUBLAS or CUSPARSE, and GPUFBS is almost 3 times faster than the implementation of FBS using the CUS PARSE library.

TL;DR: Experimental results show that the proposed GEMV‐Adaptive and GEMv‐T‐ Adaptive mitigate the performance fluctuations of the implementations in the CUBLAS library, always have high performance, and outperform the most recently proposed G EMV and GemV‐T kernels by Gao et al, respectively, for all test matrices.

TL;DR: In this paper, an adaptive optimization model of preconditioned conjugate gradient (PCG) on multi-GPUs is proposed, which is based on the profile-based optimization model for each component of the PCG algorithm.