Publisher's Synopsis
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
Modern computing hardware comes equipped with multicore CPUs and GPUs that can process numerous instruction sets simultaneously. Parallel computing takes advantage of this now-standard computer architecture to execute multiple operations at the same time, offering the potential for applications that run faster, are more energy efficient, and can be scaled to tackle problems that demand large computational capabilities. But to get these benefits, you must change the way you design and write software. Taking advantage of the tools, algorithms, and design patterns created specifically for parallel processing is essential to creating top performing applications.about the book
Parallel and High Performance Computing is an irreplaceable guide for anyone who needs to maximize application performance and reduce execution time. Parallel computing experts Robert Robey and Yuliana Zamora take a fundamental approach to parallel programming, providing novice practitioners the skills needed to tackle any high-performance computing project with modern CPU and GPU hardware. Get under the hood of parallel computing architecture and learn to evaluate hardware performance, scale up your resources to tackle larger problem sizes, and deliver a level of energy efficiency that makes high performance possible on hand-held devices. When you're done, you'll be able to build parallel programs that are reliable, robust, and require minimal code maintenance.This book is unique in its breadth, with discussions of parallel algorithms, techniques to successfully develop parallel programs, and wide coverage of the most effective languages for the CPU and GPU. The programming paradigms include MPI, OpenMP threading, and vectorization for the CPU. For the GPU, the book covers OpenMP and OpenACC directive-based approaches and the native-based CUDA and OpenCL languages.
what's inside
- Steps for planning a new parallel project
- Choosing the right data structures and algorithms
- Addressing underperforming kernels and loops
- The differences in CPU and GPU architecture
