RNA pseudoknot prediction is an algorithm for RNA sequence search and alignment. An important building block towards pseudoknot prediction is RNA secondary structure prediction. The difficulty of extending the secondary structure prediction algorithm to a parallel program is (1) it has complicated data dependences, and (2) it has a large data set that typically cannot fit completely in main memory. In this paper, we propose a new out-of-core, distributed-memory algorithm for RNA secondary structure prediction. Its novelty lies in its redundant file scheme, I/O-reducing in-core buffer mechanism, and dynamic load balancing algorithm. Experimental results obtained on 16 Sun UltraSPARC IIIi nodes provide evidence that our approach achieves good speedup. Furthermore, we found that counterintuitively, the size of the in-memory buffer is critical to efficiency of the parallel program.