Advanced Python Scientific Computing: Profiling, Cython, Numba, and Distributed Analytics (Machine Learning Mastery Toolkit)

Dwayne Flores

ISBN 13: 9798287426484

Published by Independently published, 2025

About this Item

Seller Inventory # LU-9798287426484

Bibliographic Details

• Title: Advanced Python Scientific Computing: Profiling, Cython, Numba, and Distributed Analytics (Machine Learning Mastery Toolkit)
• Publisher: Independently published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798287426484
• Binding: Paperback
• Condition: New

About this title

Synopsis

Are you frustrated by Python’s performance limits when tackling large-scale simulations, data analysis, or machine learning tasks? Do you long for the speed of compiled languages without sacrificing Python’s clarity?

Advanced Python Scientific Computing: Profiling, Cython, Numba, and Distributed Analytics offers a practical roadmap to transform your code into a high-performance powerhouse. This book equips you with proven techniques—from pinpointing performance bottlenecks to harnessing GPUs and scaling across clusters—so you can tackle computationally intensive scientific workflows with confidence.

What This Book Delivers

• Profiling Mastery: Identify time- and memory-critical hotspots using cProfile, line_profiler, memory_profiler, and tracemalloc. Learn to interpret profiling data and target your optimization efforts where they matter most.

• Cython Acceleration: Convert Python functions into C-level extensions. Understand setup tools, static typing, memoryviews, and interfacing with C libraries to boost loop-intensive code.

• Numba JIT and GPU Offloading: Write familiar Python while benefiting from LLVM-based just-in-time compilation. Compare @njit and @jit, leverage prange for multicore parallelism, and deploy @cuda.jit kernels for GPU-accelerated computations.

• Distributed Analytics with Dask and MPI4Py: Scale NumPy and Pandas patterns to clusters. Build Dask task graphs, configure schedulers, tune performance with chunking and data locality, and integrate MPI4Py for hybrid HPC workflows.

• Modern Toolchain Integration: Port array code to CuPy, explore automatic differentiation and JIT compilation with JAX, and manage large datasets using HDF5, Zarr, and Apache Arrow.

Why You’ll Benefit

• Transform slow prototypes into production-ready, performance-optimized applications.

• Gain hands-on experience with industry-standard tools for profiling, JIT compilation, and distributed computing.

• Achieve near-C performance in Python, tap GPU power, and run analytics on clusters or in serverless environments.

• Build resilient, reproducible workflows with best practices in environment management, testing, and CI/CD.

Ready to elevate your Python scientific computing skills and accelerate your research or production pipelines? Add Advanced Python Scientific Computing to your library today and start writing code that’s both clear and lightning-fast.