Introduction
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
Appendix A-References
A.1 Chapter 1: Why parallel computing?
A.2 Chapter 2: Planning for parallelism
A.3 Chapter 3: Performance limits and profiling
Published with GitBook
Introduction
Parallel and High Performance Computing
最近发现了这本电子书,买了个中文版发现翻译的有点不太好,不自量力尝试翻译这本好书。
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