Numerical Kernels on a Spatial Accelerator: A Study of Tenstorrent Wormhole
This work shows AI accelerators have potential for scientific computing, though it's an incremental study of one specific architecture.
The paper implemented three numerical kernels on Tenstorrent's Wormhole AI accelerator and composed them into a conjugate gradient solver, demonstrating that AI accelerators can compete with Nvidia GPUs for traditional scientific computing workloads.
As AI accelerators gain prominence, their potential for traditional scientific computing workloads remains unclear. This paper explores Tenstorrent's Wormhole architecture, a spatial computing platform designed for neural network acceleration, by implementing three numerical kernels and composing them into a conjugate gradient solver. We present architecture-specific optimizations for sparse numerical algorithms, evaluate their performance against Nvidia GPUs, and expose both challenges and opportunities in porting numerical methods to spatial architectures. Our results demonstrate that AI accelerators merit consideration for workloads traditionally dominated by CPUs and GPUs, and more work should be invested in understanding the capabilities of these architectures and making them accessible to the scientific computing community.