13 citations · 24 across the 8 of their papers we have counts for
8 papers · 1 filter
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…
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…
A Survey on Error-Bounded Lossy Compression for Scientific Datasets
Sheng Di, Jinyang Liu, Kai Zhao +23
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error…
High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation
Jinyang Liu, Sheng Di, Kai Zhao +7
Error-bounded lossy compression has been identified as a promising solution for significantly reducing scientific data volumes upon users' requirements on data distortion. For the…
FT-GEMM: A Fault Tolerant High Performance GEMM Implementation on x86 CPUs
Shixun Wu, Yujia Zhai, Jiajun Huang +2
General matrix/matrix multiplication (GEMM) is crucial for scientific computing and machine learning. However, the increased scale of the computing platforms raises concerns about…
Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs
Shixun Wu, Yujia Zhai, Jinyang Liu +4
General Matrix Multiplication (GEMM) is a crucial algorithm for various applications such as machine learning and scientific computing, and an efficient GEMM implementation is esse…