torch-sla: Differentiable Sparse Linear Algebra with Adjoint Solvers and Sparse Tensor Parallelism for PyTorch

The article announces the release of torch-sla, an open-source library for differentiable sparse linear algebra in PyTorch, addressing the lack of a unified solution for this domain. It features an autograd-aware API supporting various solvers across five backends (SciPy, Eigen, cuDSS, CuPy, and a PyTorch-native solver), enabling batched solves and distributed multi-GPU execution with automatic device dispatch. This library is significant for practitioners as it facilitates efficient integration of sparse linear algebra techniques into scientific machine learning workflows, enhancing scalability and performance.