An Efficient Storage Format for Storing Configuration Interaction Sparse Matrices on CPU/GPU
Mohammed Mahmoud, Mark R. Hoffmann, Hassan Reza · 2017
Sparse matrix-vector multiplication (SpMV) can be used to solve diverse-scaled linear systems and eigenvalue problems that exist in numerous and varying scientific applications. One of the scientific applications that SpMV is known as Configuration Interaction (CI). CI is a linear method for solving the nonrelativistic Schrödinger equation for quantum chemical multi-electron systems and it can deal with the ground state as well as multiple excited states. A typical CI sparse matrix requires a significant large matrix for detecting and capturing more electron correlation. In this paper, we have developed a hybrid approach to reduce the space requirement of CI sparse matrices. The proposed model includes a newly-developed hybrid format for storing CI sparse matrices on the CPU/GPU. In addition to the new developed format, the proposed model includes the SpMV kernel for multiplying the CI matrix by vector using the C language and the Compute Unified Device Architecture (CUDA) platform. We have gauged the newly developed model in terms of two primary factors, memory usage and performance.