标题： 加速 Fortran 代码：将 Coarray Fortran 与 CUDA Fortran 和 OpenMP 集成的方法 显示英文标题

标题： Accelerating Fortran Codes: A Method for Integrating Coarray Fortran with CUDA Fortran and OpenMP

摘要： Fortran在科学计算中的重要性要求制定策略，以确保遗留代码在高性能计算系统上高效运行，并使该语言对开发新的高性能代码保持吸引力。 Coarray Fortran (CAF)，作为Fortran 2008标准中引入的并行编程部分，通过一种对Fortran程序员熟悉的语法促进了分布式内存并行性，简化了从单处理器到多处理器编码的转换。 本研究专注于创新和改进一种并行编程方法，该方法融合了Intel Coarray Fortran、Nvidia CUDA Fortran和OpenMP的优势，分别用于分布式内存并行性、高速GPU加速和共享内存并行性。 我们考虑了可分页和固定内存的管理、NUMA多处理器中的CPU-GPU亲和性以及与速度优化的健壮编译器接口。 我们通过将该方法应用于并行化泊松求解器来展示它，并将该方法、实现和扩展性能与消息传递接口（MPI）进行比较，发现CAF提供了相似的速度，但实现更简单。 对于新代码，这种方法为优化并行计算提供了一条更快的途径。 对于遗留代码，它简化了向并行计算的过渡，使其能够转化为可扩展的高性能计算应用程序，而无需进行广泛的重新设计或添加额外的语法。 显示英文摘要

摘要： Fortran's prominence in scientific computing requires strategies to ensure both that legacy codes are efficient on high-performance computing systems, and that the language remains attractive for the development of new high-performance codes. Coarray Fortran (CAF), part of the Fortran 2008 standard introduced for parallel programming, facilitates distributed memory parallelism with a syntax familiar to Fortran programmers, simplifying the transition from single-processor to multi-processor coding. This research focuses on innovating and refining a parallel programming methodology that fuses the strengths of Intel Coarray Fortran, Nvidia CUDA Fortran, and OpenMP for distributed memory parallelism, high-speed GPU acceleration and shared memory parallelism respectively. We consider the management of pageable and pinned memory, CPU-GPU affinity in NUMA multiprocessors, and robust compiler interfacing with speed optimisation. We demonstrate our method through its application to a parallelised Poisson solver and compare the methodology, implementation, and scaling performance to that of the Message Passing Interface (MPI), finding CAF offers similar speeds with easier implementation. For new codes, this approach offers a faster route to optimised parallel computing. For legacy codes, it eases the transition to parallel computing, allowing their transformation into scalable, high-performance computing applications without the need for extensive re-design or additional syntax.