This is data supporting results from the paper submission Designing a GPU-Accelerated Communication Layer for Efficient Fluid-Structure Interaction Computations on Heterogenous Systems that was accepted to appear in The International Conference for High Performance Computing Networking Storage and Analysis (SC'24). The data was collected through running our proprietary code HARVEY on the Polaris (Argonne National Laboratory) and Frontier (Oak Ridge National Laboratory) supercomputers. The data represents performance information that profiles different computational aspects of our code when running at scale on the Polaris and Frontier systems. Data is stored in CSV files automatically generated by HARVEY's internal profiler tools.
Publication abstract:
As biological research demands simulations with increasingly larger cell counts optimizing these models for large-scale deployment on heterogeneous supercomputing resources becomes crucial. This requires the redesign of fluid-structure interaction tasks written around distributed data structures built for CPU-based systems where design flexibility and overall memory footprint are key considerations to instead be performant on CPU-GPU machines. This paper describes the trade-offs of offloading communication tasks to the GPUs and the corresponding changes to the underlying data structures required along with new algorithms that significantly reduce time-to-solution. At scale performance of our GPU implementation is evaluated on both the Polaris and Frontier leadership systems. Real-world workloads involving millions of deformable cells are evaluated. We analyze the competing factors that come into play when designing a communication layer for a fluid-structure interaction code including code efficiency complexity and GPU memory demands and offer advice to other high performance computing applications facing similar decisions.
Publication abstract:
As biological research demands simulations with increasingly larger cell counts optimizing these models for large-scale deployment on heterogeneous supercomputing resources becomes crucial. This requires the redesign of fluid-structure interaction tasks written around distributed data structures built for CPU-based systems where design flexibility and overall memory footprint are key considerations to instead be performant on CPU-GPU machines. This paper describes the trade-offs of offloading communication tasks to the GPUs and the corresponding changes to the underlying data structures required along with new algorithms that significantly reduce time-to-solution. At scale performance of our GPU implementation is evaluated on both the Polaris and Frontier leadership systems. Real-world workloads involving millions of deformable cells are evaluated. We analyze the competing factors that come into play when designing a communication layer for a fluid-structure interaction code including code efficiency complexity and GPU memory demands and offer advice to other high performance computing applications facing similar decisions.