Session

DescriptionNovel hybrid scalable scientific algorithms are needed with the advent of a variety of novel accelerators, including GPUs and FPGAs, as well as with the growth in size of quantum computing devices, neuromorphic chips, and various AI-specific processors. This myriad of devices requires a unified approach that allows efficient and scalable hybrid approaches combining classical and novel computing paradigms to be implemented at scale. These extreme-scale heterogeneous systems require these novel scientific algorithms to hide the complexity, hide network and memory latency, have advanced communication, and have no synchronization points where possible. With the advent of AI in the past few years, the need for such scalable mathematical methods and algorithms for such hybrid architectures that are able to handle data and compute-intensive applications at scale becomes even more important.