advanced python scientific computing de flores dwayne (9 résultats)

Etat : New.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : New.

Paperback. Etat : new. Paperback. 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 DeliversProfiling 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 atnjit and atjit, leverage prange for multicore parallelism, and deploy atcuda.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 BenefitTransform 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New. Print on Demand.

Paperback. Etat : new. Paperback. 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 DeliversProfiling 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 atnjit and atjit, leverage prange for multicore parallelism, and deploy atcuda.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 BenefitTransform 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.