To improve the performance of the sea ice model in the Community Earth System Model (CESM) under a heterogeneous computing environment, this work conducts an in-depth study on the memory access optimization of the Elastic-Viscous-Plastic (EVP) dynamics model in Community Ice Code(CICE) on the SW39000 heterogeneous many-core processor, which is deployed in the new-generation Sunway supercomputer. The processor’s complex on-chip heterogeneous architecture, multi-level memory hierarchy, and unique inter-core communication mechanism present significant challenges for the parallel optimization of the sea ice dynamics simulation. To address the inefficiencies caused by diverse data access patterns, a differentiated processing strategy based on data read/write characteristics is proposed to reduce unnecessary data transfers. In addition, to alleviate load imbalance arising from the sparsity of sea ice boundary update data, a local dynamic compression method incorporating the probability density of data sparsity is designed. This method dynamically compresses data according to the probability density of Direct Memory Access (DMA) data transfers, thereby reducing communication volume and balancing the workload across slave cores. Finally, to enhance the computational intensity of the slave cores and reduce data dependencies between master and slave cores, an operator fusion algorithm based on Remote Memory Access (RMA) communication is introduced to achieve efficient data caching and transmission between operators. Experimental results demonstrate that, under the standard gx3 grid configuration, the optimized EVP model achieves a 27.54×speedup over the serial version running on a single master core when executed with a single core group. Multi-core group parallel tests validate the excellent scalability of the proposed optimization strategies, achieving up to a 123.93×speedup with a 10-core group, while also exhibiting effective load balancing in terms of both clock cycles and instruction counts across the slave-core array.

Abstract Compared with CUDA and OpenCL, OpenACC has the advantages of simple programming, openness, and good portability for GPU acceleration. An OpenMP/OpenACC implementation for molecular dynamics of silicon crystal on GPUs is proposed. First, to make effective use of vectorization and streaming, data structure conversion and data dependence elimination are designed. Second, the parallel version on the single GPU is realized by adding OpenACC guidance sentences, with very few modifications. Third, a patch block strategy is proposed to realize the parallel version on single machine multi‐GPUs using OpenMP+OpenACC, which greatly simplifies the construction of shadow area and the exchange of shadow area data. Experimental results show that 23 to 25 speedup is achieved for the single GPU at different scales over the serial program on Intel(R) Xeon(R) CPU E5‐2690 v4, and 6.37 speedup is achieved over the single GPU when the number of atoms reaches 2,097,152 on 8GPUs on single machine.