Leveraging LLNL’s El Capitan and Tuolumne supercomputers, scientists generated over 41 million protein structure predictions for OpenFold3’s self-distillation training. The framework delivered structure predictions on the AMD APUs at the heart of our newest systems 17x faster than the original inference pipeline, making massive dataset builds plausible.

However, these models were distilled during El Capitan’s open-science “shakeout” phase and improved not only the models but also the machine. For example, ElMerFold workloads served as a comprehensive stress test for the machine, helping uncover hardware bottlenecks, validate end-to-end APU and interconnect performance, and refine system configurations. This real-world debugging accelerated readiness for production science runs and informed both LLNL and AMD on where to tune El Capitan for peak reliability.

Additionally, pushing millions of APU/GPU-accelerated inferences on El Capitan drove fixes that benefit all NNSA workloads—both AI and more traditional modeling and simulation. The project was an early large-scale user of the HPE Rabbit configurable near-node storage capability on El Capitan, also driving fixes and improvements to the benefit of all future work. Finally, the LLNL/AMD collaboration yielded optimizations that are now being applied to the software stack, including memory management and data movement."