85 citations · 89 across the 6 of their papers we have counts for
6 papers
Roundoff error analysis of the double exponential formula-based method for the matrix sign function
Tomoya Miyashita, Shuhei Kudo, Yusaku Yamamoto
In this paper, we perform a roundoff error analysis of an integration-based method for computing the matrix sign function recently proposed by Nakaya and Tanaka. The method express…
A fast and accurate computation method for reflective diffraction simulations
Shuhei Kudo, Yusaku Yamamoto, Takeo Hoshi
We present a new computation method for simulating reflection high-energy electron diffraction and the total-reflection high-energy positron diffraction experiments. The two experi…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
White Paper from Workshop on Large-scale Parallel Numerical Computing Technology (LSPANC 2020): HPC and Computer Arithmetic toward Minimal-Precision Computing
Roman Iakymchuk, Daichi Mukunoki, Artur Podobas +15
In numerical computations, precision of floating-point computations is a key factor to determine the performance (speed and energy-efficiency) as well as the reliability (accuracy…
A Parallelizable Energy-Preserving Integrator MB4 and Its Application to Quantum-Mechanical Wavepacket Dynamics
Tsubasa Sakai, Shuhei Kudo, Hiroto Imachi +3
In simulating physical systems, conservation of the total energy is often essential, especially when energy conversion between different forms of energy occurs frequently. Recently…
Multiscale Universal Interface: A Concurrent Framework for Coupling Heterogeneous Solvers
Yu-Hang Tang, Shuhei Kudo, Xin Bian +2
Concurrently coupled numerical simulations using heterogeneous solvers are powerful tools for modeling multiscale phenomena. However, major modifications to existing codes are ofte…