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Accelerating Scientific Computing: GPU Optimization Strategies, Challenges, and Performance Outcomes
DescriptionGPUs are transforming scientific computing by delivering substantial speedups and energy savings. This presentation outlines the development of GPU computing strategies for a leading CFD software. Initially, we identified and optimized computational bottlenecks suitable for GPU acceleration, using an offload model. To overcome limitations imposed by Amdahl's law, we developed a GPU-native solver architecture with streamlined APIs, ensuring seamless integration with existing workflows.
Our optimization strategy also accounts for diverse GPU platforms, implementing platform-specific enhancements for NVIDIA, AMD, and Intel architectures. Scalability was achieved through advanced load-balancing algorithms and improved inter-GPU communication, enabling efficient parallelization for large-scale simulations.
We present results demonstrating significant speedups and energy savings compared to CPU-based methods, highlighting the transformative potential of GPUs in enabling faster, more complex simulations.
Our optimization strategy also accounts for diverse GPU platforms, implementing platform-specific enhancements for NVIDIA, AMD, and Intel architectures. Scalability was achieved through advanced load-balancing algorithms and improved inter-GPU communication, enabling efficient parallelization for large-scale simulations.
We present results demonstrating significant speedups and energy savings compared to CPU-based methods, highlighting the transformative potential of GPUs in enabling faster, more complex simulations.
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