Spatula: A Hardware Accelerator for Sparse Matrix Factorization
Axel Feldmann, Daniel Sánchez · 2023
Solving sparse systems of linear equations is a crucial component in many science and engineering problems, like simulating physical systems. Sparse matrix factorization dominates a large class of these solvers. Efficient factorization algorithms have two key properties that make them challenging for existing architectures: they consist of small tasks that are structured and compute-intensive, and sparsity induces long chains of data dependences among these tasks. Data dependences make GPUs struggle, while CPUs and prior sparse linear algebra accelerators also suffer from low compute throughput.