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.

翻译：随着人工智能加速器日益突出，其在传统科学计算工作负载中的潜力仍不明朗。本文通过实现三个数值内核并将其组合成共轭梯度求解器，探索了专为神经网络加速设计的空间计算平台——Tenstorrent的Wormhole架构。我们提出了针对稀疏数值算法的架构特定优化，评估了其与Nvidia GPU的性能表现，并揭示了将数值方法移植到空间架构中的挑战与机遇。研究结果表明，人工智能加速器值得在传统由CPU和GPU主导的工作负载中被考虑，应投入更多精力理解这些架构的能力，并使其为科学计算社区所用。