Parallel and High Performance Computing 159996

 Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours—or even days—of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware.

About the technology

Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency.

About the book

Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. You’ll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You’ll master the data structures and algorithms best suited for high performance computing and learn techniques that save energy on handheld devices. You’ll even run a massive tsunami simulation across a bank of GPUs.

What's inside

    Planning a new parallel project

    Understanding differences in CPU and GPU architecture

    Addressing underperforming kernels and loops

    Managing applications with batch scheduling

For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran.

About the author

Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences.

 PART 1 INTRODUCTION TO PARALLEL COMPUTING

1 Why parallel computing?

2 Planning for parallelization

3 Performance limits and profiling

4 Data design and performance models

5 Parallel algorithms and patterns

PART 2 CPU: THE PARALLEL WORKHORSE

6 Vectorization: FLOPs for free

7 OpenMP that performs

8 MPI: The parallel backbone

PART 3 GPUS: BUILT TO ACCELERATE

9 GPU architectures and concepts

10 GPU programming model

11 Directive-based GPU programming

12 GPU languages: Getting down to basics

13 GPU profiling and tools

PART 4 HIGH PERFORMANCE COMPUTING ECOSYSTEMS

14 Affinity: Truce with the kernel

15 Batch schedulers: Bringing order to chaos

16 File operations for a parallel world

17 Tools and resources for better code