Unleash the Full Power of Python to Run Faster Code, Scale Smarter, and Compute Without Limits. Key Features ● Get a free one-month digital subscription to www.avaskillshelf.com ● Master end-to-end Python parallelism from multithreading and multiprocessing to distributed computing on GPUs, clusters, and the cloud. ● Accelerate real-world workloads using cutting-edge frameworks like Ray, Dask, PyTorch, Spark, Modin, Joblib, and CUDA. ● Deploy high-performance pipelines at scale with Kubernetes, serverless computing, FPGAs, and emerging quantum acceleration techniques. Book Description Python is the backbone for data science, AI, and cloud computing and the demand for speed and scalability has never been higher. That’s why mastering parallel and high-performance programming is essential to transform Python into a tool that meets modern performance demands. Building on the success of the first edition, Parallel and High Performance Programming with Python (2nd Edition) expands and modernizes the original work, adding new frameworks, deployment patterns, and acceleration techniques for next-generation computing. You’ll begin by mastering the core concepts of parallelism, threading, and multiprocessing, then move into asynchronous programming for responsive and efficient workloads. The book guides you through distributed Python across clusters, followed by deep dives into GPU acceleration using CUDA and PyTorch. You’ll explore real-world applications in data science and artificial intelligence, and learn how to scale pipelines seamlessly with Ray, Modin, and Spark. Advanced chapters introduce Joblib optimization, Kubernetes, and serverless scaling for cloud-native workloads, and cutting-edge topics such as FPGA acceleration and quantum computing, giving you a future-ready performance toolkit. Packed with hands-on examples, benchmarks, and deployment-ready best practices, this second edition helps you turn everyday Python into a high-performance, production-grade system. What you will learn ● Design and optimize high-performance Python applications using parallelism, concurrency, and async patterns. ● Profile, diagnose, and eliminate CPU, I/O, and memory bottlenecks for real-world workloads. ● Accelerate compute-intensive tasks using CUDA kernels, PyTorch tensors, NumPy vectorization, and GPU-enabled deep learning workflows. ● Build and scale distributed systems seamlessly with Dask, Ray, Apache Spark, and Modin for massive data processing. ● Deploy and orchestrate compute pipelines on Kubernetes, AWS Lambda, and Azure Functions for cost-efficient scalability. ● Integrate advanced acceleration technologies like Joblib, FPGA workflows, and quantum simulation frameworks to stay ahead of the curve. Who is This Book For? This book is tailored for data scientists, machine learning engineers, Python developers, and backend programmers who need to boost performance and scalability in their applications. It also serves data and cloud engineers, DevOps/MLOps professionals, HPC specialists, and distributed systems developers building high-throughput pipelines. Technical architects, research engineers, and quantitative analysts will find advanced design patterns to optimize workloads across CPUs, GPUs, clusters, FPGAs, and serverless or quantum environments. Table of Contents 1. Introduction to Parallel Programming 2. Parallel Programming with Threads 3. Parallel Programming with Processes 4. Asynchronous Programming 5. Distributed Python 6. GPU Programming with Python 7. Parallel Computing Applications 8. Parallel Computing for Data Science 9. Parallel Computing for Artificial Intelligence 10. Future of Parallel Programming 11. Modern Frameworks for Parallelism and Distribution: Ray and Modin 12. PySpark 13. Joblib 14. Parallelization on Cloud and Serverless Systems 15. Parallel Programming with FPGAs 16. Introduction to Quantum Computing and Quantum Architectures Index About the Author Fabio Nelli is a Data Analyst at a private research center. He holds a bachelor’s degree in Automation Engineering (computer science track) and a master’s degree in Organic Chemistry. The author of several books on data science and Python programming, he publishes technical articles on a regular basis. Previously, he worked as a consultant, automation specialist, and analyst–programmer for international organizations. His interests include high-performance computing, large-scale data processing, cloud computing, and machine-learning applications.
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