FourPhonon_GPU：一种用于计算声子散射率与热导率的GPU加速框架
FourPhonon_GPU: A GPU-accelerated framework for calculating phonon scattering rates and thermal conductivity
AI总结：
针对声子散射计算成本过高的问题，研究提出基于FourPhonon的GPU加速框架FourPhonon_GPU，采用CPU-GPU异构策略实现散射率计算超25倍、总运行超10倍加速，且精度无损、可扩展性好，为声子输运建模和材料发现提供高效工具。
摘要：
准确预测声子散射对于理解热输运性质至关重要。然而，这类计算的计算成本——尤其是四声子散射的计算——在涉及大量声子支和散射过程时，往往会高得令人望而却步。在本研究中，我们提出FourPhonon_GPU，这是一款基于FourPhonon软件包开发、用于三声子和四声子散射率计算的GPU加速框架。通过利用OpenACC并采用CPU-GPU异构计算策略，我们将海量可并行化任务高效卸载到GPU，同时用CPU执行过程枚举和控制密集型操作。我们的方法在散射率计算步骤实现了超过25倍的加速，总运行时速度提升超过10倍，且未牺牲精度。在多种GPU架构上的基准测试证实了该方法的可扩展性，也凸显了并行化策略与硬件能力相匹配的重要性。本研究为声子输运建模提供了一种高效且准确的计算工具，也为加速材料发现开辟了路径。
英文摘要：
Accurately predicting phonon scattering is crucial for understanding thermal transport properties. However, the computational cost of such calculations, especially for four-phonon scattering, can often be more prohibitive when large number of phonon branches and scattering processes are involved. In this work, we present FourPhonon_GPU, a GPU-accelerated framework for three-phonon and four-phonon scattering rate calculations based on the FourPhonon package. By leveraging OpenACC and adopting a heterogeneous CPU-GPU computing strategy, we efficiently offload massive, parallelizable tasks to the GPU while using the CPU for process enumeration and control-heavy operations. Our approach achieves over 25x acceleration for the scattering rate computation step and over 10x total runtime speedup without sacrificing accuracy. Benchmarking on various GPU architectures confirms the method's scalability and highlights the importance of aligning parallelization strategies with hardware capabilities. This work provides an efficient and accurate computational tool for phonon transport modeling and opens pathways for accelerated materials discovery.
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