cuSOLVER

cuSOLVER is a collection of GPU-accelerated routines for solving dense and sparse linear systems, as well as for performing eigenvalue computations. It is part of NVIDIA's CUDA toolkit, designed to leverage the parallel processing power of NVIDIA GPUs for scientific and engineering applications. cuSOLVER provides high-performance implementations of fundamental linear algebra operations, making them accessible to developers through a familiar API.

The library offers functionalities for various types of linear algebra problems. For dense matrices, it includes solvers for systems of linear equations (like LU, Cholesky, and QR factorizations), eigenvalue problems (symmetric and non-symmetric), and singular value decomposition (SVD). For sparse matrices, cuSOLVER provides solutions for linear systems and eigenvalue problems using iterative and direct methods.

cuSOLVER's primary advantage lies in its ability to achieve significant speedups compared to CPU-based implementations, especially for large-scale problems. This is due to the massively parallel architecture of GPUs. Developers can integrate cuSOLVER routines into their CUDA applications to accelerate computationally intensive tasks, thereby reducing execution time and enabling the analysis of larger datasets. The library is designed to be user-friendly for those familiar with standard linear algebra libraries like LAPACK and ScaLAPACK.