We describe an efficient parallel and vector algorithm for solving huge eigen-vector problems in quantum chemistry. An automatically adaptive, single-vector, iterative diagonalization method was also developed to reduce the memory requirement and avoid an I/O bottleneck. Our initial full-configuration interaction calculation solved for an eigenvector with 65 billion coefficients and was performed on 432 MSPs of the Oak Ridge National Laboratory Cray-X1. One matrixvector multiplication took about 4 minutes, with 25 iterations being required for a tightly converged result. The aggregate performance was 3.4TFLOP/s (62% of peak speed).

A distributed data parallel full CI program is described The implementation of the FCI algorithm is organized in a combined Cl driven approach With extra computation we were able to avoid redundant communication, and convert the collective communication into more efficient point-to-point communication. The network performance is further optimized by improved DDI library. Examples show very good speedup performance on 16 node PC clusters. The application of the code is also demonstrated.