CUDA Fortran for Scientists and Engineers

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