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Performance evaluation of GPU-enhanced Jacobi algorithm implementations for optimizing CFD Poisson partial differential equation solving | IEEE Conference Publication | IEEE Xplore

Performance evaluation of GPU-enhanced Jacobi algorithm implementations for optimizing CFD Poisson partial differential equation solving


Abstract:

The current work presents an in-depth analysis of several optimizations using GPU parallel computing applied to the Jacobi method for solving Poisson partial differential...Show More

Abstract:

The current work presents an in-depth analysis of several optimizations using GPU parallel computing applied to the Jacobi method for solving Poisson partial differential equations in computational fluid dynamics (CFD). We expand on previous CPU-parallelized Jacobi algorithm research, exploring four GPU-optimized Jacobi method variants: single-threaded, multi-threaded, multi-GPU and a norm-based stopping criterion kernel. These implementations are benchmarked against a multi-threaded CPU baseline. Results indicate that, whereas the single-threaded GPU version is slower than the CPU baseline, multi-threaded GPU versions achieve significant speed gains, especially for larger grid sizes. The multi-GPU version doubles memory bandwidth, enhancing performance for extensive computations, despite overhead for smaller matrices. The norm-stopping criterion kernel offers early convergence for small matrices but at a high overhead cost. Profiling confirms a memory-bound bottleneck, suggesting single-precision and optimized memory access as improvements. Ultimately, multi-threaded GPU kernels substantially outperform the CPU baseline for large-scale CFD problems, establishing GPUs as efficient accelerators for the Jacobi algorithm.
Date of Conference: 10-12 October 2024
Date Added to IEEE Xplore: 11 November 2024
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Conference Location: Sinaia, Romania

References

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