Author: Dr. C. Vijesh Joe

ISBN: 978-81-991955-5-4

DOI: https://doi.org/10.59646/pc/429

Date of Publication: August 22, 2025

About the Book:

The book Parallel Computing for AI, ML, and Big Data Analytics provides a deep and structured exploration of how parallelism drives efficiency, scalability, and innovation across modern computational systems, addressing the ever-increasing demands of artificial intelligence, machine learning, and big data applications. Starting with an introduction to the foundations of parallel computing, it covers the types of parallelism, architectures, and programming models while tracing the historical journey from single-core processors to today’s GPUs, TPUs, and heterogeneous accelerators. The text emphasizes the crucial role of parallelism in handling massive datasets, training complex models like CNNs, RNNs, and transformers, and managing computational challenges such as scalability, latency, and energy efficiency. With dedicated units on hardware architectures, programming frameworks, and real-world case studies, the book explains how CPUs, GPUs, FPGAs, ASICs, and distributed clusters contribute to parallel processing, and how frameworks such as CUDA, OpenMP, MPI, Apache Spark, and TensorFlow enable practical implementation of high-performance workloads. Advanced discussions on data, model, pipeline, and hybrid parallelism illustrate techniques used to scale large models like GPT-3, while topics like distributed SGD, gradient checkpointing, and communication optimization highlight methods to reduce bottlenecks in training and inference. Big data analytics is examined through paradigms like MapReduce, streaming, in-memory processing, Spark, and parallel graph analytics, linking them to AI/ML workflows such as fraud detection, sentiment analysis, and large-scale biological network modeling. Performance optimization is another central theme, with units covering Amdahl’s and Gustafson’s laws, speedup analysis, load balancing, scalability, bottleneck analysis, and memory optimization strategies that ensure efficient use of parallel resources. The book also highlights emerging trends such as federated learning, decentralized model training, quantum parallelism, edge AI, IoT acceleration, lightweight model deployment, and distributed hyperparameter optimization, reflecting the cutting-edge directions of research and practice. In its final sections, it presents a range of applications and case studies, spanning healthcare (genomic data, medical imaging, clinical decision support), finance (risk modeling, algorithmic trading), autonomous systems (drones, self-driving vehicles, SLAM), and NLP (transformer scaling, sentiment analysis, text generation), while also addressing the critical aspect of energy-efficient AI. By blending theory with hands-on insights into architectures, frameworks, and real-world applications, the book not only equips readers with the conceptual foundations of parallel computing but also provides the applied knowledge necessary for leveraging parallelism in solving the most pressing challenges in AI, ML, and big data analytics.