As HPC systems move into the exascale era an increasing diversity of processing hardware is being deployed. The last decade saw the ascendance of NVIDIA GPU-accelerated systems among the largest scale HPC systems and spurred the need for application developers to consider approaches to performance portability that preserved developer productivity. This challenge has been compounded in the last several years by the introduction of the first two exascale systems, Frontier and Aurora (\#2 and \#3 on the November 2024 Top 500 list respectively). These systems utilize new and different GPUs, with the AMD MI-250X GPU on Frontier and the Intel Data Center GPU Max 1550 on Aurora. This study investigates the performance and qualitative performance portability of$\mathbf{1 2}$HPC and ML applications on three large scale HPC systems that utilize GPUs from the three different vendors: Frontier (AMD), Aurora (Intel), and Polaris (NVIDIA A100). The performance of these applications is evaluated on single GPU, single node, and multinode scales on each of the systems. We show that the figures-of-merit (FOMs) of the applications on a single GPU of Aurora and Frontier ranged from$0.9-4 x$and$0.8-2.5 x$, respectively, the performance on a GPU of Polaris. We also show that the FOMs on a single node of Aurora and Frontier ranged from 1.3-6.3x and 0.8-2.6x, respectively, a single node of Polaris. The applications were scaled up to 512 nodes showing good scaling efficiency across the board. Finally, we discuss useful concepts and experiences gained in running diverse applications on diverse HPC systems.

Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble calibration in weather forecasting compared to deterministic methods, yet have so far proven difficult to scale stably at high resolutions. We introduce AERIS, a 1.3 to 80B parameter pixel-level Swin diffusion transformer to address this gap, and SWiPe, a generalizable technique that composes window parallelism with sequence and pipeline parallelism to shard window-based transformers without added communication cost or increased global batch size. On Aurora (10,080 nodes), AERIS sustains 10.21 ExaFLOPS (mixed precision) and a peak performance of 11.21 ExaFLOPS with 1 × 1 patch size on the 0.25° ERA5 dataset, achieving 95.5% weak scaling efficiency, and 81.6% strong scaling efficiency. AERIS outperforms the IFS ENS and remains stable on seasonal scales to 90 days, highlighting the potential of billion-parameter diffusion models for weather and climate prediction.

We present a scalable, end-to-end workflow for protein design. By augmenting protein sequences with natural language descriptions of their biochemical properties, we train generative models that can be preferentially aligned with protein fitness landscapes. Through complex experimental-and simulation-based observations, we integrate these measures as preferred parameters for generating new protein variants and demonstrate our workflow on five diverse supercomputers. We achieve >1 ExaFLOPS sustained performance in mixed precision on each supercomputer and a maximum sustained performance of 4.11 Ex-aFLOPS and peak performance of 5.57 ExaFLOPS. We establish the scientific performance of our model on two tasks: (1) across a predetermined benchmark dataset of deep mutational scanning experiments to optimize the fitness-determining mutations in the yeast protein HIS7, and (2) in optimizing the design of the enzyme malate dehydrogenase to achieve lower activation barriers (and therefore increased catalytic rates) using simulation data. Our implementation thus sets high watermarks for multimodal protein design workflows.