System and Environment Managementī.2 nvidia-smi System Management InterfaceĪppendix C. Applications of Fast Fourier TransformĪppendix B. Performance Measurement and MetricsĢ.2 Instruction, bandwidth, and latency bound kernelsģ.4 Memory optimization example: matrix transposeĤ.2 Multi-GPU Programming with MPI Part II: Case StudiesĦ.1 Nine-Point 1D finite difference stencilĬhapter 7. NVIDIA Corporation, Santa Clara, CA Table of ContentsĬompanion Site Part I: CUDA Fortran Programmingġ.4 Determining CUDA hardware features and limitsĬhapter 2.
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![simply fortran cuda simply fortran cuda](https://www.lumi-supercomputer.eu/content/uploads/2021/04/hipify_figure1.jpg)
All of this is done in Fortran, without having to rewrite in another language.
#Simply fortran cuda how to
To help you add CUDA Fortran to existing Fortran codes, the book explains how to understand the target GPU architecture, identify computationally intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking.