An Out-of-Core Eigensolver on SSD-equipped Clusters

Obtaining highly accurate predictions on properties of light atomic nuclei using the Configuration Interaction (CI)approach requires computing few extremal eigenpairs of a large many-body nuclear Hamiltonian matrix, Ĥ. A forefront challenge in CI calculations is the massive size of Ĥ and its eigenvectors. The emergence of clusters equipped with non-volatile NAND-flash memory based solid state drives (SSD) presents unique opportunities. In this paper, we present the implementation details of an out-of-core eigensolver using a novel distributed out-of-core linear algebra framework, called DOoC+LAF. The framework provides an easy-to-use high-level application interface for linear algebra operations while providing efficient execution by orchestrating pipelined execution of computation, communication and I/O. We demonstrate the effectiveness of our out-of-core eigensolver implemented using DOoC+LAF by reporting performance results on large-scale eigenvalue problems arising in nuclear structure calculations.