2 papers
physics.chem-ph2024
Very-Large-Scale GPU-Accelerated Nuclear Gradient of Time-Dependent Density Functional Theory with Tamm-Dancoff Approximation and Range-Separated Hybrid Functionals
Inkoo Kim, Daun Jeong, Leah Weisburn +11
Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical…
cs.DC2024
Pipette: Automatic Fine-grained Large Language Model Training Configurator for Real-World Clusters
Jinkyu Yim, Jaeyong Song, Yerim Choi +4
Training large language models (LLMs) is known to be challenging because of the huge computational and memory capacity requirements. To address these issues, it is common to use a…