A multiple-GPU based parallel independent coefficient reanalysis method and applications for vehicle design.

The main limits of reanalysis method using CUDA (Compute Unified Device Architecture) for large-scale engineering optimization problems are low efficiency on single GPU and memory bottleneck of GPU. To breakthrough these bottlenecks, an efficient parallel independent coefficient (IC) reanalysis method is developed based on multiple GPUs platform. The IC reanalysis procedure is reconstructed to accommodate the use of multiple GPUs. The matrices and vectors are successfully partitioned and prepared for each GPU to achieve good load balance as well as little communication between GPUs. This study also proposes an effective technique to overlap the computation and communication by using non-blocking communication strategy. GPUs would continue their succeeding tasks while communication is still carried out simultaneously. Furthermore, the CSR format is used in each GPU for saving the memory. Finally, large-scale vehicle design problems are implemented by the developed solver. According to the test results, the multi-GPU based IC reanalysis method has potential capability for handling the real large scale problem and reducing the design cycle. [ABSTRACT FROM AUTHOR]