Discover how to tackle Python parallel computing challenges and optimize your software development process with effective solutions.

What is this book about? Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user ...

ipyparallel, also known as IPython Parallel, is yet another tool for parallel computing in Python. However, it's more than just parallel Python, it's parallel IPython, and this adds interactivity to ...

Book Description Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, ...

The best parallel processing libraries for Python Ray: Parallelizes and distributes AI and machine learning workloads across CPUs, machines, and GPUs.

Pierre Glaser from INRIA gave this talk at EuroPython 2019. "Modern hardware is multi-core. It is crucial for Python to provide high-performance parallelism. This talk will expose to both ...

Parallel computing can be divided into four types: bit-level, instruction-level, data, and task parallelism. Parallelism has long been used in high-performance computing, but it has recently gained ...

Understand the basics of how parallel computing works Judge when it’s appropriate to use different parallel programming techniques Use the basics of a selection of parallel processing libraries in ...

Discover how to tackle the complexities of parallel computing in Python, from GIL restrictions to scalability and performance optimization.

Learn the first steps within Python to highly specialized concepts Explore examples and code snippets taken from typical programming situations within scientific computing. Delve into essential ...