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.