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.

Atomistic-scale simulations are prominent scientific applications that require the repetitive execution of a computationally expensive routine to calculate a system's potential energy. Prior work shows that these expensive routines can be replaced with a machine-learned surrogate approximation to accelerate the simulation at the expense of the overall accuracy. The exact balance of speed and accuracy depends on the specific configuration of the surrogate-modeling workflow and the science itself, and prior work leaves it up to the scientist to find a configuration that delivers the required accuracy for their science problem. Unfortunately, due to the underlying system dynamics, it is rare that a single surrogate configuration presents an optimal accuracy/latency trade-off for the entire simulation. In practice, scientists must choose conservative configurations so that accuracy is always acceptable, forgoing possible acceleration. As an alternative, we propose Proxima, a systematic and automated method for dynamically tuning a surrogate-modeling configuration in response to real-time feedback from the ongoing simulation. Proxima estimates the uncertainty of applying a surrogate approximation in each step of an iterative simulation. Using this information, the specific surrogate configuration can be adjusted dynamically to ensure maximum speedup while sustaining a required accuracy metric. We evaluate Proxima using a Monte Carlo sampling application and find that Proxima respects a wide range of user-defined accuracy goals while achieving speedups of 1.02--5.5X relative to a standard

Approximate computing addresses many of the identified challenges for exascale computing, leading to performance improvements that may include changes in fidelity of calculation. In this paper, we examine approximate approaches for a range of DOE-relevant computational problems run on a variety of architectures as a proxy for the wider set of exascaleclass applications.We show anticipated improvements in computational and memory performance and in power savings. We also assess application correctness when operating under conditions of reduced precision, and show that this is within acceptable bounds. Finally, we discuss the trade space between performance, power, precision and resolution for these mini-apps, and optimized solutions attained within given constraints, with positive implications for application of approximate computing to exascale-class problems.