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📘 CUDA Fortran for Scientists and Engineers [2011] Greg Ruetsch, Massimiliano Fatica

This document in intended for scientists and engineers who develop or maintain computer simulations and applications in Fortran, and who would like to harness parallel processing power of graphics processing units (GPUs) to accelerate their code. The goal here is to provide the reader with the fundamentals of GPU programming using CUDA Fortran as well as some typical examples without having the task of developing CUDA Fortran code becoming an end in itself. The CUDA architecture was developed by NVIDIA to allow use of the GPU for general purpose computing without requiring the programmer to have a background in graphics. There are many ways to access the CUDA architecture from a programmer’s perspective, either through C/C++ from CUDA C and Open CL, or through Fortran using PGI’s CUDA Fortran. This document pertains to the latter approach. PGI’s CUDA Fortran should be distinguished from the PGI Accelerator product, which is a directive based approach to using the GPU. CUDA Fortran is simply the Fortran analog to CUDA C. The reader of this book should be familiar with Fortran 90 concepts, such as modules, derived types, and array operations. However, no experience with parallel programming (on the GPU or otherwise) is required. Part of the appeal of parallel programming on GPUs using CUDA is that the programming model is simple and novices can get parallel code up and running very quickly. CUDA is a hybrid programming model, where both GPU and CPU are utilized, so CPU code can be incrementally ported to the GPU. This document is divided into two main sections, the first is a tutorial on CUDA Fortran programming, from the basics of writing CUDA Fortran code to some tips on optimization. The second part of this document is a collection of case studies that demonstrate how the principles in the first section are applied to real-world examples.

📗 CUDA Fortran для инженеров и научных работников [2014] Грегори Рутш, Массимилиано Фатика


Fortran – один из важнейших языков программирования для высокопроизводительных вычислений, для которого было разработано множество популярных пакетов программ для решения вычислительных задач. Корпорация NVIDIA совместно с The Portland Group (PGI) разработали набор расширений к языку Fortran, которые позволяют использовать технологию CUDA на графических картах NVIDIA для ускорения вычислений.

Книга демонстрирует всю мощь и гибкость этого расширенного языка для создания высокопроизводительных вычислений. Не требуя никаких предварительных познаний в области параллельного программирования, авторы скрупулезно, шаг за шагом, раскрывают основы создания высокопроизводительных параллельных приложений, попутно поясняя важные архитектурные детали современного графического процессора – ускорителя вычислений.

Издание предназначено для инженеров, научных работников, программистов, в также будет полезно студентам вузов соответствующих специальностей. #математика #CUDA #GPU #графика #наука #Fortran #моделирование #физика #physics #инженерия #параллельные_вычисления

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📘 CUDA Fortran for Scientists and Engineers [2011] Greg Ruetsch, Massimiliano Fatica

This document in intended for scientists and engineers who develop or maintain computer simulations and applications in Fortran, and who would like to harness parallel processing power of graphics processing units (GPUs) to accelerate their code. The goal here is to provide the reader with the fundamentals of GPU programming using CUDA Fortran as well as some typical examples without having the task of developing CUDA Fortran code becoming an end in itself. The CUDA architecture was developed by NVIDIA to allow use of the GPU for general purpose computing without requiring the programmer to have a background in graphics. There are many ways to access the CUDA architecture from a programmer’s perspective, either through C/C++ from CUDA C and Open CL, or through Fortran using PGI’s CUDA Fortran. This document pertains to the latter approach. PGI’s CUDA Fortran should be distinguished from the PGI Accelerator product, which is a directive based approach to using the GPU. CUDA Fortran is simply the Fortran analog to CUDA C. The reader of this book should be familiar with Fortran 90 concepts, such as modules, derived types, and array operations. However, no experience with parallel programming (on the GPU or otherwise) is required. Part of the appeal of parallel programming on GPUs using CUDA is that the programming model is simple and novices can get parallel code up and running very quickly. CUDA is a hybrid programming model, where both GPU and CPU are utilized, so CPU code can be incrementally ported to the GPU. This document is divided into two main sections, the first is a tutorial on CUDA Fortran programming, from the basics of writing CUDA Fortran code to some tips on optimization. The second part of this document is a collection of case studies that demonstrate how the principles in the first section are applied to real-world examples.

📗 CUDA Fortran для инженеров и научных работников [2014] Грегори Рутш, Массимилиано Фатика


Fortran – один из важнейших языков программирования для высокопроизводительных вычислений, для которого было разработано множество популярных пакетов программ для решения вычислительных задач. Корпорация NVIDIA совместно с The Portland Group (PGI) разработали набор расширений к языку Fortran, которые позволяют использовать технологию CUDA на графических картах NVIDIA для ускорения вычислений.

Книга демонстрирует всю мощь и гибкость этого расширенного языка для создания высокопроизводительных вычислений. Не требуя никаких предварительных познаний в области параллельного программирования, авторы скрупулезно, шаг за шагом, раскрывают основы создания высокопроизводительных параллельных приложений, попутно поясняя важные архитектурные детали современного графического процессора – ускорителя вычислений.

Издание предназначено для инженеров, научных работников, программистов, в также будет полезно студентам вузов соответствующих специальностей. #математика #CUDA #GPU #графика #наука #Fortran #моделирование #физика #physics #инженерия #параллельные_вычисления

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