Exploring spectral element methods on the Tenstorrent RISC-V accelerator
2026-08-24 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors looked into using a special RISC-V based accelerator card called the Tenstorrent Wormhole for a part of a scientific computing program that solves math problems related to fluid flow. They focused on a specific calculation called the AX kernel from Nekbone, which is important for simulating physical systems. At first, they found that moving data on the host computer slowed down the process, especially when computing gradients in one direction. By applying two optimization methods, the authors greatly sped up the calculations, achieving better performance while using much less power compared to a standard Intel CPU.
RISC-VHigh Performance ComputingTenstorrent WormholeNekbonePoisson operatorTensix coresData transpositionGradient computationGFLOPSXeon Platinum CPU
Authors
Daniyal Arshad, Nick Brown
Abstract
The growing availability of commodity RISC-V hardware has sparked interest in its use for High Performance Computing (HPC), with PCIe accelerator cards offering a practical near-term pathway to adoption. The Tenstorrent Wormhole is one example, with dedicated vector and matrix units across 128 Tensix cores, and is widely available. In this paper, we explore porting the AX kernel of Nekbone, a widely used HPC mini-application derived from the Gordon Bell Prize-winning Nek5000 spectral element solver, onto the Wormhole accelerator. This kernel evaluates the Poisson operator, and we describe the mapping of the algorithm onto the Tensix. The initial performance results reveal that the host-side data transposition, required for the z-direction gradient computation, is a severe bottleneck. Consequently, we investigated two optimisation strategies that yield dramatic improvements, achieving 242.97 GFLOPS for 100000 elements across 128 Tensix cores, outperforming a 24-core Xeon Platinum CPU and drawing approximately 7 times less power.