Design of a large-scale storage-class RRAM system
Proceedings of the 27th international ACM conference on International conference on supercomputing
Exploring the future of out-of-core computing with compute-local non-volatile memory
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
| Hi-index | 0.00 |
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, H. A forefront challenge in CI calculations is the massive size of H 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.