FlashFFTStencil: Bridging Fast Fourier Transforms to Memory-Efficient Stencil Computations on Tensor Core Units - Han, Haozhi; Li, Kun; Cui, Wei; Bai, Donglin; Zhang, Yiwei; Yuan, Liang; Chen, Yifeng; Zhang, Yunquan; Cao, Ting; Yang, Mao (ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 2025)
While Tensor Core Units (TCUs) excel in AI tasks, their application to HPC algorithms like stencil computations faces significant challenges due to sparsity, which leads to underutilization and exacerbates memory-bound limitations. This paper introduces FlashFFTStencil, a memory-efficient stencil computing system designed to bridge FFT to fully-dense stencil computations on TCUs. Aimed at bound shifting, FlashFFTStencil comprises three key techniques: Kernel Tailoring on HBM fuses distinct kernels to enhance parallelism while reducing memory transfer and footprint; Architecture Aligning on SMEMrestructures FFT-based stencil computations into dense matrix multiplications tailored for shared memory architecture; Computation Streamlining on TCU optimizes TCU utilization and thread parallelism by minimizing pipeline stalls and maximizing register reuse. Notably, a distinctive extension is FlashFFTStencil’s ability to enable theoretically unrestricted temporal fusion by FFT. Results show that FlashFFTStencil achieves effective sparsity-free bound shifting, with an average speedup of 2.57x over the state-of-the-art. FlashFFTStencil pioneers a new era in unifying computational patterns within the HPC landscape and bridges them with cutting-edge AI-driven hardware innovations like TCUs.