This work focuses on the acceleration of a finite-volume Computational Fluid Dynamics (CFD) solver using CPU-GPU heterogeneous code. The authors present a high-level description of the CFD solver called SENSEI, its discretization, and the CPU-GPU heterogeneous computing workflow in SENSEI utilizing MPI and OpenACC. To estimate the performance of the heterogeneous implementation, the authors propose a performance model for CPU-GPU heterogeneous computing that requires ghost cell exchange. They demonstrate the scaling performance of CPU-GPU heterogeneous computing by comparing it with pure multi-CPU/GPU performance for a supersonic inlet test case. The results show that leveraging both CPUs and GPUs as workers together improves performance compared to using pure CPUs or GPUs. The authors also provide suggestions for application users interested in accelerating their own scientific computing simulations by leveraging the computational power of CPUs and GPUs. Additionally, they offer feedback for hardware architects interested in designing better CPU-GPU heterogeneous systems for heterogeneous computing. In terms of results, one CFD test case considered is the supersonic flow through a simplified 2D 30-degree inlet. The authors compare the CPU serial solution, multi-CPU parallel solution, GPU serial solution, and multi-GPU parallel solution. They find that all solutions have a relative difference less than 10^-12. The inflow conditions for this test case are provided in Table 1. Furthermore, factors influencing the overall performance of CPU-GPU heterogeneous computing are discussed. One such factor is determining the optimal single GPU worker over single CPU worker workload ratio. This can be achieved by running cases with different workload ratios. Overall, this study demonstrates that utilizing both CPUs and GPUs as workers can significantly improve performance compared to using only one type of processor. The proposed performance model helps estimate the advantages of leveraging both CPUs and GPUs accurately. This work provides valuable insights for application users looking to accelerate their scientific computing simulations and hardware architects aiming to design better CPU-GPU heterogeneous systems.
- Focus on acceleration of a finite-volume CFD solver using CPU-GPU heterogeneous code
- Description of the CFD solver called SENSEI and its discretization
- Utilization of MPI and OpenACC for CPU-GPU heterogeneous computing workflow in SENSEI
- Proposal of a performance model for CPU-GPU heterogeneous computing with ghost cell exchange
- Comparison of scaling performance with pure multi-CPU/GPU performance for a supersonic inlet test case
- Improvement in performance by leveraging both CPUs and GPUs as workers compared to using pure CPUs or GPUs
- Suggestions for application users interested in accelerating their own scientific computing simulations using CPUs and GPUs
- Feedback for hardware architects interested in designing better CPU-GPU heterogeneous systems
- Comparison of different solutions (CPU serial, multi-CPU parallel, GPU serial, multi-GPU parallel) for a supersonic flow test case with relative difference less than 10^-12
- Discussion on factors influencing overall performance of CPU-GPU heterogeneous computing, including optimal workload ratio between single GPU worker and single CPU worker
- Demonstration that utilizing both CPUs and GPUs can significantly improve performance compared to using only one type of processor
- Proposal of a performance model to accurately estimate advantages of leveraging both CPUs and GPUs
- Valuable insights for application users looking to accelerate scientific computing simulations and hardware architects aiming to design better CPU-GPU heterogeneous systems.
This is a very complex topic that may be difficult for a six-year-old to understand. However, I can provide simplified definitions for some of the important words
- Acceleration: Making something go faster.
- Finite-volume CFD solver: A computer program that solves problems related to fluid dynamics using a specific method called finite volume.
- CPU-GPU heterogeneous code: Computer code that is designed to run on both central processing units (CPUs) and graphics processing units (GPUs) at the same time.
- Discretization: Breaking something down into smaller parts or steps.
- MPI: A way for computers to communicate and work together in parallel.
- OpenACC: A programming standard that helps with parallel computing on GPUs.
- Performance model: A way of predicting how well something will perform or work.
- Scaling performance: How well something works as you increase its size or complexity.
- Supersonic inlet test case: An experiment or simulation involving air flowing faster than the speed of sound.
These are just a few definitions to help explain some of the key words in the summary. The overall topic is quite advanced and may require further explanation from an adult or teacher."
Accelerating a Finite-Volume Computational Fluid Dynamics (CFD) Solver Using CPU-GPU Heterogeneous Code
Computational fluid dynamics (CFD) is an important tool used to simulate the behavior of fluids. It has many applications in engineering, science, and medicine. In this paper, the authors present a high-level description of a CFD solver called SENSEI and its discretization. They also discuss how to leverage both CPUs and GPUs as workers together for improved performance using MPI and OpenACC.
SENSEI: A High-Level Description
The authors describe SENSEI as a finite-volume CFD solver that uses an explicit time integration scheme with second order accuracy in space and time. The governing equations are solved on structured grids using either single or multiple processors/GPUs depending on the user's requirements. SENSEI utilizes MPI for distributed memory parallelism across multiple nodes, while OpenACC is used for shared memory parallelism within each node. Additionally, ghost cell exchange between CPU and GPU workers is required to ensure correct results when running heterogeneous code on both types of processors simultaneously.
Performance Modeling for CPU-GPU Heterogeneous Computing
To estimate the performance of the heterogeneous implementation, the authors propose a performance model that requires ghost cell exchange between CPU and GPU workers. This model helps accurately predict the advantages of leveraging both CPUs and GPUs compared to using only one type of processor. Factors influencing overall performance are discussed including determining optimal single GPU worker over single CPU worker workload ratio by running cases with different ratios.
Demonstrating Scaling Performance with Supersonic Inlet Test Case
The authors demonstrate scaling performance by comparing pure multi-CPU/GPU performance with their proposed heterogeneous computing approach for a supersonic inlet test case (Table 1). The inflow conditions are provided in Table 1 which shows relative differences less than 10^−12 for all solutions considered - serial solution on CPUs, serial solution on GPUs, multi-CPU parallel solution, multi-GPU parallel solution - indicating accurate results were obtained from all solutions tested.
Conclusion
This study demonstrates that utilizing both CPUs and GPUs as workers can significantly improve performance compared to using only one type of processor when running scientific computing simulations such as CFD problems requiring finite volume methods like SENSEI does here. The proposed performance model helps estimate these advantages accurately while providing valuable insights into factors influencing overall system efficiency such as determining optimal single GPU worker over single CPU worker workload ratio through experimentation with different ratios.. This work provides useful information not only for application users looking to accelerate their own simulations but also hardware architects interested in designing better systems specifically tailored towards heterogeneous computing tasks like those presented here involving CFDsolver acceleration via CPU-GPU combinations
