This work bridges the gap between multi-GPU computing and high-productivity, performance-portable programming solutions. Our goal is to enhance scientific applications with a productive and portable solution—program once, deploy everywhere—for multi-GPU programming with no cost to programmability. To accomplish this, we implemented JACC.Multi, which is part of the Julia for ACCelerators (JACC) performance-portable framework. JACC. Multi is the only high-level, portable metaprogramming solution that targets multi-GPU environments and is integrated in a readily accessible programming language (e.g., Julia language). With transparent GPU-to-GPU communication, JACC. Multi is optimized for scientific application workloads and is portable for NVIDIA and AMD accelerators. For the evaluation, we use two modern multi-GPU systems: Hudson, which features two NVIDIA H100 Hopper GPUs per node, and Frontier, which features four AMD MI250X GPUs per node, each with two Graphics Compute Dies (GCDs) for a total of eight GCDs per node. Additionally, as part of the evaluation, we use JACC (one GPU), MPI+JACC, and JACC. Multi codes that implement well-known and widely used scientific algorithms/kernels such as the conjugate gradient algorithm and an explicit forward Euler solver that requires GPU-to-GPU communication. Overall, JACC. Multi codes achieve better performance than MPI+JACC codes and significant speedups over JACC (one GPU), with up to 1.9× on Hudson and 6× on Frontier.

The purpose of this study is to discuss the experiential lessons associated with adopting scientific workflows in the Exascale Additive Manufacturing project (ExaAM) through the lens of Perceived Characteristic of Innovation (PCI). Besides the implementation, the factors we considered critical to the adoption of the workflow are provenance, sustainable automation, implementation challenges, and integration/compatibility challenges. Through conversations and interviews among the program managers, project leads, and software engineers, we have developed critical insight and strategies to overcome the obstacles and augment the successful adoption and long-term use of these workflows in ExaAM and beyond. We hope our work will pave the way for others in the research community to develop and use workflows in their respective science domains.