Azul: An Accelerator for Sparse Iterative Solvers Leveraging Distributed On-Chip Memory
Axel Feldmann, Courtney Golden, Yifan Yang, Joel Emer, Daniel Sánchez · 2024
Solving sparse systems of linear equations is a fundamental primitive in many numeric algorithms. Iterative solvers provide an efficient way of solving large, highly sparse systems. However, iterative solvers are inefficient on existing architectures because they perform computations with (1) poor short-term reuse, which causes frequent off-chip memory traffic; and (2) challenaing data dependences, which limit parallelism. We present Azul, a hardware accelerator that achieves high arithmetic intensity by keeping data in distributed on-chip SRAM. Azul is organized as a grid of tiles, each with a small memory and a simple processing element (PE). This enables keeping solver data on-chip across iterations, achieving high reuse. We present a novel scheduling algorithm that maps data and computation across PEs to avoid communication bottlenecks while achieving high parallelism, and a specialized PE that achieves high utilization of arithmetic units. When tested on a representative set of matrices for sparse iterative solvers, Azul is gmean 217 × faster than state-of-the art GPU implementations, 159× faster than a previously proposed accelerator for sparse iterative solvers, and 90 × faster than a previously proposed distributed-SRAM accelerator.