High Performance Programming — Parallel Systems Path

BUNDLE OBJECTIVE
This bundle is designed for Software Engineers, Data Scientists, HPC Specialists, Research Engineers, and Python/Julia Developers aiming to master parallel and distributed computing techniques. Through hands-on projects and practical examples, learners will gain expertise in high-performance computing, parallel programming, asynchronous operations, GPU optimization, and distributed data processing. By the end, readers will be equipped to design and implement efficient, scalable, and high-performance applications for data science and scientific computing.

KEY FEATURES
Hands-on parallel programming with Python for high performance.
Master distributed computing techniques with Julia.
Implement asynchronous, multithreaded, and GPU solutions.
Build scalable data-driven applications for HPC environments.
Explore statistical models, machine learning, and Bayesian inference.

DESCRIPTION
The High Performance Programming — Parallel Systems Path bundle is a 2-book collection crafted for professionals advancing from intermediate to expert in parallel and distributed computing.

Parallel and High Performance Programming with Python introduces parallel programming, multithreading, asynchronous programming, GPU computing, and distributed Python, providing practical tools to optimize programs for high-performance applications.

Ultimate Parallel and Distributed Computing with Julia For Data Science guides readers through Julia programming essentials, data manipulation with arrays and DataFrames.jl, advanced statistical models, machine learning with MLJ.jl and MLBase.jl, Bayesian inference with Turing.jl, and visualization with Plots.jl.

Together, these titles equip learners with the practical skills and theoretical understanding needed to implement high-performance, scalable, and data-driven solutions across scientific computing and data-intensive applications.

WHAT WILL YOU LEARN
Implement parallel programming techniques using Python.
Optimize programs with multithreading, asynchronous tasks, and GPUs.
Handle large datasets efficiently using Julia arrays and DataFrames.jl.
Apply advanced statistical models and machine learning in Julia.
Perform Bayesian inference and visualize results with Julia
Build scalable and distributed high-performance applications.

WHO THIS BUNDLE IS FOR
This bundle is for software engineers, HPC specialists, data scientists, and developers seeking to harness the full potential of parallel and distributed computing. Prior experience with Python or Julia is recommended.

TABLE OF CONTENTS
Parallel and High Performance Programming with Python
Introduction to Parallel Programming
Threads, Processes, and Parallelism
Asynchronous Programming in Python
Distributed Python Programming
GPU Programming and Optimization
Real-World Projects and Applications

Ultimate Parallel and Distributed Computing with Julia For Data Science
Julia Syntax, Variables, and Functions
Data Handling with Julia Arrays and DataFrames.jl
Statistical Models and Advanced Analytics
Machine Learning with MLJ.jl and MLBase.jl
Bayesian Inference with Turing.jl
Data Visualization with Plots.jl
Practical Data Science Projects