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Book Summary
This textbook provides a comprehensive introduction to parallel computing, covering fundamental algorithms, programming models, and modern hardware platforms. It bridges theory and practice, guiding readers from basic concepts to hands-on implementation on multicore CPUs and GPUs. Ideal for students and professionals seeking a solid foundation in parallel programming.
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About Introduction to Parallel Computing: From Algorithms to Programming on State-of-the-Art Platforms
Introduction to Parallel Computing offers a rigorous yet accessible treatment of parallel computing principles and practices. The authors systematically explore parallel algorithm design, performance analysis, and programming models such as OpenMP, MPI, and CUDA. The book emphasizes practical implementation on state-of-the-art platforms, including multicore processors and graphics processing units. Readers learn to identify parallelism, design efficient algorithms, and optimize code for real-world systems. With a balance of theoretical foundations and hands-on examples, this text equips readers with the skills to harness the power of modern parallel architectures. It is suitable for advanced undergraduate and graduate courses, as well as for practitioners transitioning to parallel programming.
Who should read Introduction to Parallel Computing: From Algorithms to Programming on State-of-the-Art Platforms?
This book is ideal for advanced undergraduate and graduate students in computer science, engineering, and computational science. It also serves professionals and researchers who need to understand parallel computing concepts and apply them to real-world problems. Readers should have basic knowledge of sequential programming and algorithms. Instructors will find it a valuable resource for course design, while self-learners benefit from its clear explanations and practical examples.
Pros & Cons
Pros
- ✓ Comprehensive coverage of parallel algorithms and programming models
- ✓ Balanced blend of theory and practical implementation
- ✓ Includes modern platforms like multicore CPUs and GPUs
- ✓ Clear explanations with illustrative examples
- ✓ Suitable for both classroom use and self-study
Cons
- ✗ Assumes prior knowledge of sequential programming
- ✗ Some advanced topics may require additional background
- ✗ Limited coverage of emerging parallel paradigms
- ✗ Exercises could be more varied
- ✗ No companion code repository mentioned
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Frequently asked questions
What programming languages are used in this book?
The book primarily uses C/C++ with parallel extensions like OpenMP, MPI, and CUDA, but the concepts apply to other languages as well.
Does this book cover GPU programming?
Yes, it includes coverage of GPU programming using CUDA, with examples and case studies.
Is this book suitable for self-study?
Yes, it is designed to be accessible for self-learners, with clear explanations and practical examples, though some background in sequential programming is recommended.
What are the prerequisites?
Readers should have a basic understanding of algorithms, data structures, and programming in a language like C or C++.
Does the book include exercises?
Yes, each chapter includes exercises to reinforce concepts and provide hands-on practice.
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