5 New Parallel Computing Books Shaping 2025

Unlike most parallel computing books that dive straight into complex APIs, Tom Deakin and Timothy G. Mattson bring clarity to GPU programming with OpenMP by focusing on performance portability across diverse hardware. You’ll learn how to write code that efficiently harnesses CPUs and GPUs together using a unified, portable model, with practical examples in C, C++, and Fortran. The book’s tutorial style breaks down both foundational concepts and advanced topics, such as heterogeneous programming and the OpenMP 5.2 API, making it accessible whether you’re new to GPU programming or aiming to optimize production-level applications. This approach benefits software developers and researchers eager to adapt their code seamlessly across multi-architecture systems without getting bogged down in vendor-specific details.

After analyzing the rapid evolution of parallel computing technologies, Cybellium TM developed this guide to equip you with both foundational concepts and the latest exam-focused knowledge. You'll gain clarity on core principles like concurrency models, synchronization, and performance optimization, alongside emerging trends shaping the field today. The book’s structured chapters break down complex topics such as distributed memory architectures and parallel algorithms, making it suitable whether you’re a student prepping for exams or a professional refreshing your skills. If you seek a thorough understanding tailored specifically for assessment success in parallel computing, this book offers a focused, up-to-date resource without unnecessary fluff.

This tailored book delves into the forefront of parallel computing as it stands in 2025, focusing especially on GPU programming advancements. It explores the latest breakthroughs and emerging techniques that redefine how parallel tasks are executed, providing a personalized journey that matches your background and interests. The content covers evolving GPU architectures, novel concurrency approaches, and cutting-edge programming models, ensuring you engage deeply with the newest discoveries. By concentrating on your specific goals and areas of fascination, this book offers a focused and enthusiastic exploration of contemporary parallel computing developments that are shaping the future of high-performance computation.

Kirill Bobrov approaches concurrency not as a dry academic subject but as a lively symphony of tasks working together, making this book a fresh take on a complex topic. You’ll learn to write efficient, scalable code by understanding threading, asynchronous programming, and parallel processing, all illustrated with Python examples and engaging stories like the “Pizza-making Tale of Concurrency.” Bobrov’s background in high-load web applications informs his practical insights into hardware architectures and common concurrency pitfalls such as race conditions and deadlocks. This book suits you if you want to grasp concurrency fundamentals without jargon and immediately apply them to real-world projects.

Drawing from his deep expertise in Python and data science, Tim Peters developed this book to demystify parallel computing using Dask, a powerful tool reshaping Python workflows. You’ll learn how to accelerate tasks with parallel mapping, manage distributed systems, and integrate Dask seamlessly with popular libraries like Pandas, Scikit-Learn, and PyTorch. Peters dedicates chapters to practical techniques such as scaling machine learning models, leveraging GPUs via RAPIDS and JAX, and building robust data pipelines, making this a solid choice if you work with large datasets and want to optimize performance. While the book assumes some Python knowledge, it’s especially useful for data scientists and engineers aiming to operationalize parallel computing efficiently.

What if everything you knew about computational geometry was wrong? A Aggarwal, together with C O'Dunlaing and C Yap, challenges traditional approaches by integrating parallel computing techniques to accelerate geometric computations. You dive into algorithmic design and analysis tailored for parallel architectures, gaining insights into optimizing geometry problems that matter in graphics, robotics, and spatial data analysis. The book’s concise chapters focus on both theory and practical implementation strategies, making it suited for researchers and practitioners aiming to leverage parallelism in geometry tasks. If you're involved in high-performance computing or geometric algorithm development, this book offers focused expertise without unnecessary digressions.

This tailored book explores the forefront of parallel computing developments expected in 2025, focusing on emerging discoveries and innovative techniques shaping the future of the field. It examines cutting-edge insights in GPU programming models, distributed computing frameworks, and concurrency methods, all personalized to match your existing knowledge and specific interests. By concentrating on your goals, this book reveals how new hardware architectures and software paradigms interact to redefine performance and scalability. The tailored content ensures you engage deeply with topics that matter most to you, offering a focused pathway through the rapidly evolving landscape of parallel computing. This personalized approach enhances your ability to anticipate and adapt to future technological shifts with confidence.