7 Parallel Computing Books That Separate Experts from Amateurs

David B. Kirk's extensive background as an NVIDIA Fellow and graphics hardware innovator, combined with Wen-mei W. Hwu's academic expertise, shapes this book into a detailed guide on programming massively parallel processors. You’ll explore how to build efficient parallel programs, starting from computational thinking through to performance optimization using CUDA and OpenCL, with real-world case studies like MRI reconstruction highlighting practical applications. This book is tailored for advanced students, software developers, and engineers aiming to deepen their mastery of GPU programming and parallel architectures. If you seek a rigorous, example-driven approach rather than surface-level coverage, this book will meet your expectations.

After years of hands-on work with parallel programming and GPU acceleration, Tom Deakin and Timothy G. Mattson developed this guide to demystify using OpenMP for GPU programming. You’ll learn how to write portable, parallel code that runs efficiently across multiple architectures, with clear examples in C, C++, and Fortran demonstrating both basic and advanced OpenMP directives. The book focuses on practical skills like combining CPU and GPU workloads for true heterogeneous programming, supported by tutorial videos and an online code repository. If you’re aiming to master GPU programming within the OpenMP model, this book offers a direct path without unnecessary complexity.

This tailored book explores fundamental and advanced concepts in parallel computing, offering a learning experience customized to your background and goals. It examines core principles such as task decomposition and synchronization, while also revealing advanced techniques including distributed memory models and performance tuning. By focusing on your interests and skill level, the book enables you to engage deeply with complex topics without unnecessary material. Through this personalized approach, it bridges expert knowledge with your unique learning needs, guiding you through both theoretical foundations and practical applications. The content is carefully synthesized to align with your objectives, providing a clear pathway to mastering parallel computing concepts and their real-world uses.

Peter Pacheco brings his extensive experience as a computer science educator to this tutorial-style guide that walks you through the essentials of parallel programming using MPI, Pthreads, and OpenMP. Instead of abstract theory, you encounter carefully designed exercises that help you build and debug both distributed and shared-memory programs, focusing on real multi-core and cluster architectures. For example, early chapters introduce small programs that gradually evolve into sophisticated examples illustrating performance evaluation techniques. This book suits students and developers who want a hands-on, practical foundation in parallel programming without getting lost in jargon or overly complex setups.

What happens when a software engineer passionate about data engineering tackles concurrency? Kirill Bobrov delivers an accessible guide that breaks down threading, asynchronous programming, and parallel processing without relying on dense jargon or heavy math. You’ll learn to write scalable concurrency systems by exploring practical examples and patterns such as race condition prevention, deadlock handling, and asynchronous communication, all illustrated through Python code. This book suits you if you’re aiming to improve performance on multicore processors or build resilient, high-load applications, whether in web development, big data, or IoT.

What started as Joe Armstrong's quest to address the challenges of concurrent systems led to this book, which guides you through Erlang's unique approach to writing scalable parallel programs. You’ll learn how to harness lightweight processes to build fault-tolerant, distributed applications that run efficiently on multicore architectures. Chapters cover everything from sequential programming foundations to advanced Erlang features like maps, Dialyzer, and OTP frameworks, giving you practical skills to handle real-world concurrency problems. This book suits software developers eager to master parallelism without prior functional programming experience, especially those working on telecommunication, web, or cloud systems.

This tailored book explores the pathway to accelerating your parallel computing skills through a focused 90-day action plan. It covers core concepts and advanced techniques in parallel computing, including task decomposition, synchronization, and performance tuning, all matched to your background and learning goals. The content examines how to identify bottlenecks and optimize code for multi-core and GPU architectures, providing a personalized synthesis that zeroes in on what matters most to you. By concentrating on your specific interests and project needs, this tailored guide reveals practical steps to enhance computational efficiency and project outcomes, making complex parallel programming concepts accessible and actionable within your unique context.

Kai Hwang's decades of experience in computer architecture and parallel processing underpin this detailed exploration of distributed and cloud computing systems. You learn how modern technologies—from clusters and grids to cloud services and peer-to-peer networks—come together to build scalable, high-performance systems. The book walks you through virtualization techniques for management and disaster recovery, supported by case studies from Amazon, Microsoft, and Google, making it particularly insightful if you're designing or managing distributed architectures. Whether you're a student or a professional engineer, the chapters provide technical depth and practical examples that sharpen your understanding of how these complex systems operate.

What started as Milan Curcic's deep involvement with meteorology and oceanography evolved into a compelling guide for developing efficient parallel applications using modern Fortran. You’ll explore practical skills like parallel programming with coarrays, advanced module development, and interoperability with C, illustrated through projects such as tsunami simulators and stock price analyzers. The book suits developers and computational scientists interested in leveraging Fortran’s powerful capabilities for high-performance computing without prior Fortran experience. It challenges the notion that Fortran is outdated by showcasing its relevance in contemporary parallel computing tasks, especially in scientific and industrial applications.