As heterogeneous supercomputing architectures leveraging GPUs become increasingly central to high-performance computing (HPC), it is crucial for computational fluid dynamics (CFD) simulations, a de-facto HPC workload, to efficiently utilize such hardware. One of the key challenges of HPC codes is performance portability, i.e. the ability to maintain near-optimal performance across different accelerators. In the context of the REFMAP project, which targets scalable, GPU-enabled multi-fidelity CFD for urban airflow prediction, this paper analyzes the performance portability of SOD2D, a state-of-the-art Spectral Elements simulation framework across AMD and NVIDIA GPU architectures. We first discuss the physical and numerical models underlying SOD2D, highlighting its computational hotspots. Then, we examine its performance and scalability in a multi-level manner, i.e. defining and characterizing an extensive full-stack design space spanning across application, software and hardware infrastructure related parameters. Single-GPU performance characterization across server-grade NVIDIA and AMD GPU architectures and vendor-specific compiler stacks, show the potential as well as the diverse effect of memory access optimizations, i.e. 0.69× - 3.91× deviations in acceleration speedup. Performance variability of SOD2D at scale is further examined on the LUMI multi-GPU cluster, where profiling reveals similar throughput variations, highlighting the limits of performance projections and the need for multi-level, informed tuning.

As heterogeneous supercomputing architectures leveraging GPUs become increasingly central to high-performance computing (HPC), it is crucial for computational fluid dynamics (CFD) simulations to maintain performance portability.In this paper, we examine the performance and scalability of CFD framework SOD2D in a crosslayer manner, i.e. across application, software and hardware infrastructure related parameters.Single-GPU performance characterization across server-grade NVIDIA and AMD GPU architectures and vendor-specific compiler stacks, show the potential as well as the diverse effect of memory access optimizations, i.e. 0.69× -3.96× deviations in acceleration speedup.Performance variability of SOD2D at scale is then further examined on the LUMI multi-GPU cluster, showcasing analogous diverse effects on throughput, demonstrating the ineffectiveness of adopting performance projections, thus underscoring the importance and necessity of cross-layer informed performance analysis and tuning for multi-GPU configurations.

Graphics Processing Units (GPUs) use massive multi-threading coupled with static scheduling to hide instruction latencies. Despite this, memory instructions pose a challenge as their latencies vary throughout the application’s execution, leading to stalls. Out-of-order (OoO) execution has been shown to effectively mitigate these types of stalls. However, prior OoO proposals involve costly techniques such as reordering loads and stores, register renaming, or two-phase execution, amplifying implementation overhead and consequently creating a substantial barrier to adoption in GPUs. This paper introduces GhOST, a minimal yet effective OoO technique for GPUs. Without expensive components, GhOST can manifest a substantial portion of the instruction reorderings found in an idealized OoO GPU. GhOST leverages the decode stage’s existing pool of decoded instructions and the existing issue stage’s information about instructions in the pipeline to select instructions for OoO execution with little additional hardware. A comprehensive evaluation of GhOST and the prior state-of-the-art OoO technique across a range of diverse GPU benchmarks yields two surprising insights: (1) Prior works utilized Nvidia’s intermediate representation PTX for evaluation; however, the optimized static instruction scheduling of the final binary form negates many purported improvements from OoO execution; and (2) The prior state-of-the-art OoO technique results in an average slowdown across this set of benchmarks. In contrast, GhOST achieves a $\mathbf{3 6 \%}$ maximum and $6.9 \%$ geometric mean speedup on GPU binaries with only a $0.007 \%$ area increase, surpassing previous techniques without slowing down any of the measured benchmarks.