5 papers
FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention
Huangliang Dai, Shixun Wu, Jiajun Huang +4
Transformer models rely on High-Performance Computing (HPC) resources for inference, where soft errors are inevitable in large-scale systems, making the reliability of the model pa…
TurboFNO: High-Performance Fourier Neural Operator with Fused FFT-GEMM-iFFT on GPU
Shixun Wu, Yujia Zhai, Huangliang Dai +4
Fourier Neural Operators (FNO) are widely used for learning partial differential equation solution operators. However, FNO lacks architecture-aware optimizations,with its Fourier l…
TurboFFT: Co-Designed High-Performance and Fault-Tolerant Fast Fourier Transform on GPUs
Shixun Wu, Yujia Zhai, Jinyang Liu +6
GPU-based fast Fourier transform (FFT) is extremely important for scientific computing and signal processing. However, we find the inefficiency of existing FFT libraries and the ab…
FT K-means: A High-Performance K-means on GPU with Fault Tolerance
Shixun Wu, Yitong Ding, Yujia Zhai +8
K-means is a widely used algorithm in clustering, however, its efficiency is primarily constrained by the computational cost of distance computing. Existing implementations suffer…
TurboFFT: A High-Performance Fast Fourier Transform with Fault Tolerance on GPU
Shixun Wu, Yujia Zhai, Jinyang Liu +6
The Fast Fourier Transform (FFT), as a core computation in a wide range of scientific applications, is increasingly threatened by reliability issues. In this paper, we introduce Tu…