Minisymposium Presentation

Over the last decade GPU architectures have dramatically improved in both performance and energy efficiency. Due largely to the rising importance of artificial intelligence (AI), especially in the areas of large language models (LLMs) and generative AI, this growth has been most pronounced in reduced precision matrix multiplication capacity, where the introduction of Tensor Cores and new datatypes has sparked a wave of innovative techniques at the juncture of AI and scientific computing. In addition to extending the reach and impact of mixed-precision algorithms, these hardware riches have sparked the development of new floating point emulation algorithms across a wide range of precisions, including but not limited to single- and double-precision. In this talk, we will look at the accuracy, performance, and energy efficiency of these methods and provide insights into the challenges and opportunities involved in making them broadly available to the scientific computing community.