Discovery must do better than that. In addition, according to the RFP, it must support everything from small workloads using 20% of the nodes to those that require the entire system. It must be expandable with “new, novel architectures,” and interoperate with and support connected DOE experimental user facilities and other ORNL Leadership Computing Facility (LCF) infrastructure.
It also must be operational before the end of Frontier’s service life, operate within OLCF’s operations and utilities budget, provide a productive programming environment for users, and “continue to make progress and lead in dramatically improving energy efficiency across the ecosystem.”
Oh, yes — and it must be AI-friendly, of course.
Discovery should “be at the forefront in supporting domain scientists and application developers as they explore and integrate transformational AI technologies to accelerate discoveries in science, energy, and security problems of national importance,” the RFP outlined.
As well, OLCF added in its RFP, “We envision a wide spectrum of use cases ranging from inverse design and control of complex systems such as power grids and nuclear reactors, to generative AI and foundational models that integrate text and images that are often unstructured, high-resolution, and from multi-modal data sources.
“Executing AI-empowered computing campaigns and workflows will place new demands on the system architecture, possibly requiring more interconnect bandwidth and an optimized storage layer that can handle very high rates of I/O operations (IOPS) focused on random reads.”
