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CUDA Fortran for Scientists and Engineers

Best Practices for Efficient CUDA Fortran Programming

CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientifi… Read more

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Description

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. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran.

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. All of this is done in Fortran, without having to rewrite in another language. Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison.

Key features

  • Leverage the power of GPU computing with PGI’s CUDA Fortran compiler
  • Gain insights from members of the CUDA Fortran language development team
  • Includes multi-GPU programming in CUDA Fortran, covering both peer-to-peer and message passing interface (MPI) approaches
  • Includes full source code for all the examples and several case studies
  • Download source code and slides from the book's companion website

Readership

Professional scientists and engineers whose research codes are in Fortran; students studying parallel programming using Fortran.

Table of contents

Dedication

Acknowledgments

Preface

Companion Site

Part I: CUDA Fortran Programming

Chapter 1. Introduction

Abstract

1.1 A brief history of GPU computing

1.2 Parallel computation

1.3 Basic concepts

1.4 Determining CUDA hardware features and limits

1.5 Error handling

1.6 Compiling CUDA Fortran code

Chapter 2. Performance Measurement and Metrics

Abstract

2.1 Measuring kernel execution time

2.2 Instruction, bandwidth, and latency bound kernels

2.3 Memory bandwidth

Chapter 3. Optimization

Abstract

3.1 Transfers between host and device

3.2 Device memory

3.3 On-chip memory

3.4 Memory optimization example: matrix transpose

3.5 Execution configuration

3.6 Instruction optimization

3.7 Kernel loop directives

Chapter 4. Multi-GPU Programming

Abstract

4.1 CUDA multi-GPU features

4.2 Multi-GPU Programming with MPI

Part II: Case Studies

Chapter 5. Monte Carlo Method

Abstract

5.1 CURAND

5.2 Computing with CUF kernels

5.3 Computing with reduction kernels

5.4 Accuracy of summation

5.5 Option pricing

Chapter 6. Finite Difference Method

Abstract

6.1 Nine-Point 1D finite difference stencil

6.2 2D Laplace equation

Chapter 7. Applications of Fast Fourier Transform

Abstract

7.1 CUFFT

7.2 Spectral derivatives

7.3 Convolution

7.4 Poisson Solver

Part III: Appendices

Appendix A. Tesla Specifications

Appendix B. System and Environment Management

B.1 Environment variables

B.2 nvidia-smi System Management Interface

Appendix C. Calling CUDA C from CUDA Fortran

C.1 Calling CUDA C libraries

C.2 Calling User-Written CUDA C Code

Appendix D. Source Code

D.1 Texture memory

D.2 Matrix transpose

D.3 Thread- and instruction-level parallelism

D.4 Multi-GPU programming

D.5 Finite difference code

D.6 Spectral Poisson Solver

References

Index

Review quotes

"This book is written for the Fortran programmer who wants to do real work on GPUs, not just stunts or demonstrations. The book has many examples, and includes introductory material on GPU programming as well as advanced topics such as data optimization, instruction optimization and multiple GPU programming. Placing the performance measurement chapter before performance optimization is key, since measurement drives the tuning and optimization process. All Fortran programmers interested in GPU programming should read this book."—Michael Wolfe, PGI Compiler Engineer

Product details

About the authors

GR

Gregory Ruetsch

Greg Ruetsch is a Senior Applied Engineer at NVIDIA, where he works on CUDA Fortran and performance optimization of HPC codes. He holds a Bachelor’s degree in mechanical and aerospace engineering from Rutgers University and a Ph.D. in applied mathematics from Brown University. Prior to joining NVIDIA, he has held research positions at Stanford University’s Center for Turbulence Research and Sun Microsystems Laboratories.
Affiliations and expertise
Senior Applied Engineer, NVIDIA

MF

Massimiliano Fatica

Massimiliano Fatica is the Director of the HPC Benchmarking Group at NVIDIA where he works in the area of GPU computing (high-performance computing and clusters). He holds a laurea in Aeronautical Engineering and a PhD in Theoretical and Applied Mechanics from the University of Rome “La Sapienza”. Prior to joining NVIDIA, he was a research staff member at Stanford University where he worked at the Center for Turbulence Research and Center for Integrated Turbulent Simulations on applications for the Stanford Streaming Supercomputer.
Affiliations and expertise
Director, HPC Benchmarking Group, NVIDIA

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