1 citations · 4 across the 8 of their papers we have counts for
8 papers
STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
Hong Wang, Jiang Yixuan, Jie Wang +3
Operator eigenvalue problems play a critical role in various scientific fields and engineering applications, yet numerical methods are hindered by the curse of dimensionality. Rece…
GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
Zhihao Xu, Srikar Chundury, Seongmin Kim +6
Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm…
An SMT Formalization of Mixed-Precision Matrix Multiplication: Modeling Three Generations of Tensor Cores
Benjamin Valpey, Xinyi Li, Sreepathi Pai +1
Many recent computational accelerators provide non-standard (e.g., reduced precision) arithmetic operations to enhance performance for floating-point matrix multiplication. Unfortu…
ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training
Yuhang Liang, Xinyi Li, Jie Ren +3
Large Language Models (LLMs) have demonstrated remarkable performance in various natural language processing tasks. However, the training of these models is computationally intensi…
FlowFPX: Nimble Tools for Debugging Floating-Point Exceptions
Taylor Allred, Xinyi Li, Ashton Wiersdorf +2
Reliable numerical computations are central to scientific computing, but the floating-point arithmetic that enables large-scale models is error-prone. Numeric exceptions are a comm…
FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators
Xinyi Li, Ang Li, Bo Fang +3
NVIDIA Tensor Cores and AMD Matrix Cores (together called Matrix Accelerators) are of growing interest in high-performance computing and machine learning owing to their high perfor…
A GPU accelerated mixed-precision Smoothed Particle Hydrodynamics framework with cell-based relative coordinates
Zirui Mao, Xinyi Li, Shenyang Hu +2
Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major p…
MPGemmFI: A Fault Injection Technique for Mixed Precision GEMM in ML Applications
Bo Fang, Xinyi Li, Harvey Dam +9
Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet such demand, one of the critical features of machine-learning-specific accelerator…