An overview of parallel processing of rectangular determinant calculation

This paper focuses on developing algorithms for parallel determinant processing, a crucial task in linear algebra and computational mathematics. The aim is to improve efficiency in high-performance computing environments by designing and analyzing algorithms that use parallel processing to expedite determinant computation for various matrices range. The research explores methods like Laplace expansion, LU decomposition, eigenvalue decomposition, Gaussian elimination, and cofactor expansion, assessing their efficiency, scalability, and applicability in different computational environments. The study employs advanced parallel programming techniques and architectures, utilizing multi-core processors with the focus aim into utilization of Chio’s method of rectangular determinants processing in parallel etc. The research also investigates the mathematical underpinnings of parallel determinant algorithms, addressing challenges like load balancing, data distribution, and synchronization. The results show significant improvements in determinant calculations efficiency, reducing computation times for large matrices.