Nuclear reactor FSI simulation faces two key challenges: "Mapping wall" bottleneck in data transfer across non-matching mesh coupling interfaces; Low hardware utilization from multi-physics solvers’ heterogeneous core tasks (compute- vs. memory-intensive). Therefore, an innovative FSI framework integrating two strategies is proposed: Scalable radial basis function mapping—restructuring the global problem into massive independent subproblems via task partitioning, preallocation, and multi-granularity load balancing to eliminate communication overhead; Dependency-aware multi-stream optimization—deeply overlapping heterogeneous solver tasks to maximize hardware utilization. It first achieves parameter transfer across ∼90,000 non-matching coupling interfaces in China Experimental Fast Reactor, with 86.36% strong scaling and 94.01% weak scaling. The combined optimizations yield ∼60% performance gain, increase strong scaling by over 20 percentage points, and achieve high weak scaling of ∼97%. Moreover, the FSI results align well with publicly available data, verifying its correctness.

The Atomic Kinetic Monte Carlo (AKMC) method provides insights into the macroscopic behavior of materials through atomistic-level simulations and finds broad applications in materials science innovation. Improving simulation scale and performance remains a consistent focus in the development of parallel AKMC software. We port the AKMC software to GPU clusters. To alleviate the memory pressure in large-scale complex system simulations, we redesign the data layout and propose the Lattice Data Compression and Vacancy Data Decompression algorithms. Additionally, We propose a multi-level pipeline scheme combined with an on-demand communication forwarding and merging strategy to reduce data transfer and communication overhead. Compared to state-of-the-art KMC software, MISA-AKMC achieves a 10.41-fold improvement in computational throughput and a 52.07-fold expansion in simulation scale. We implement the first true micrometer-scale AKMC simulation involving 20 quadrillion atoms on GPU clusters. MISA-AKMC achieves 96.03% parallel efficiency in weak scaling and 85.29% in strong scaling on 16,000 GPUs.

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for various numerical computing applications. However, most existing GPU-SpMV approaches may suffer from either long preprocessing overhead, load imbalance, format conversion, bad memory access patterns. In this paper, we proposed two new SpMV algorithms:flat andline-enhance, as well as their implementations, for GPU systems to overcome the above shortcomings. Our algorithms work directly on the CSR sparse matrix format. To achieve high performance: 1) for load balance, theflat algorithm uses non-zero splitting andline-enhance uses a mix of row and non-zero splitting; 2) memory access patterns are designed for both algorithms for data loading, storing and reduction steps; and 3) an adaptive approach is proposed to select appropriate algorithm and parameters based on matrix characteristics.