Speaker
Description
The trends in computer architecture, primarily driven by AI-based applications (most recently, large language models), has led to a rapid increase in the reduced- and mixed-precision computing capabilities of GPUs. These processors demonstrate an outsized power-efficiency (FLOPS/watt) advantage over systems almost exclusively focused upon native single- and double-precision arithmetic. Thus, there is a great deal of motivation to leverage these capabilities, through the use of various mixed-precision algorithms and emulation techniques, to facilitate greater scientific computing throughput without sacrificing accuracy. We'll touch upon a number of these approaches and present real-world case studies that provide compelling evidence in support of this path to increasing the science per watt of supercomputers.
