Parallel and Distri. Computing

📘 Parallel and Distributed Computing – (2025–2026 Edition) 📚 Parallel and Distributed Computing (2025–2026 Edition) is a comprehensive syllabus-based learning resource designed for BSCS, BSSE, BSIT students, self-learners, researchers, professionals, and freelancers exploring the principles, architectures, and applications of modern high-performance computing. The book offers a blend of theory, practical exercises, MCQs, and quizzes to help learners deeply understand how large-scale computing systems collaborate to solve complex problems efficiently. This edition covers everything from the fundamentals of parallelism and distributed systems to advanced frameworks such as CUDA, MPI, OpenMP, MapReduce, and Spark, equipping readers with both academic insight and industry-relevant skills. It bridges the gap between hardware architectures, software models, algorithms, and real-world applications, preparing students for careers in cloud computing, AI, data science, and HPC. 📂 Chapters & Topics 🔹 Chapter 1: Introduction to Parallel and Distributed Computing -Motivation, benefits, and limitations -Applications in science, AI, business, and cloud systems -Speedup models: Amdahl’s Law, Gustafson’s Law 🔹 Chapter 2: Hardware Architectures -Shared and distributed memory systems -Clusters, grids, and multicore processors -GPUs, accelerators, and CUDA basics -Cloud and edge computing infrastructures 🔹 Chapter 3: Software Architectures and Models -Threads, message passing, and distributed shared memory -Data sharing models (DSM, DSD) -Virtualization and containerization 🔹 Chapter 4: Parallel Algorithms and Design Techniques -Principles of parallel design -Synchronization and concurrency -Load balancing and granularity -Common design patterns: divide-and-conquer, pipelining 🔹 Chapter 5: Shared-Memory Programming -Threads and synchronization (locks, semaphores, monitors) -Pthreads programming and performance tuning -Deadlocks and avoidance 🔹 Chapter 6: Distributed-Memory Programming -Message Passing Interface (MPI) and PVM -Remote Procedure Calls (RPC) -Practical examples and case studies 🔹 Chapter 7: Other Parallel Programming Paradigms -OpenMP, CUDA, and GPU programming -Big Data frameworks: Hadoop, MapReduce, Spark -TensorFlow distributed training 🔹 Chapter 8: Advanced Topics in Distributed Systems -Distributed shared memory in depth -Cloud-based frameworks: AWS, Azure, Kubernetes -Enterprise-level distributed computing 🔹 Chapter 9: Case Studies and Applications -Parallel search, sorting, and AI applications -Scientific computing and HPC use cases 🔹 Chapter 10: Research Trends and Project Work -Scalability, security, and green computing -Quantum and edge AI systems -Research directions and project ideas 🌟 Why Choose this Book/App? ✅ Complete syllabus with MCQs and quizzes ✅ Covers both parallel and distributed paradigms comprehensively ✅ Includes real-world examples and modern frameworks ✅ Perfect for students, self-learners, and computing professionals ✅ Aligns with university courses and professional certifications ✍ This app is inspired by the authors: David Culler, Ananth Grama, Ian Foster, Vipin Kumar, and Andrew Tanenbaum 📥 Download Now! Master the art of high-performance and distributed computing with Parallel and Distributed Computing (2025–2026 Edition) — the complete guide to scalable, efficient, and intelligent computing systems.