6 papers
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…
ZCCL: Significantly Improving Collective Communication With Error-Bounded Lossy Compression
Jiajun Huang, Sheng Di, Xiaodong Yu +12
With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communication turns out to be a critic…
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…
gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters
Jiajun Huang, Sheng Di, Xiaodong Yu +11
GPU-aware collective communication has become a major bottleneck for modern computing platforms as GPU computing power rapidly rises. A traditional approach is to directly integrat…
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…