6 papers
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…
HiRace: Accurate and Fast Source-Level Race Checking of GPU Programs
John Jacobson, Martin Burtscher, Ganesh Gopalakrishnan
Data races are egregious parallel programming bugs on CPUs. They are even worse on GPUs due to the hierarchical thread and memory structure, which makes it possible to write code t…
Report of the DOE/NSF Workshop on Correctness in Scientific Computing, June 2023, Orlando, FL
Maya Gokhale, Ganesh Gopalakrishnan, Jackson Mayo +3
This report is a digest of the DOE/NSF Workshop on Correctness in Scientific Computing (CSC'23) held on June 17, 2023, as part of the Federated Computing Research Conference (FCRC)…
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…