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137by Sun, QimingTable of Contents: “...9.3.1 The dis module -- 9.3.2 Performance-friendly Python code -- Using enumerate for index and object iteration -- Using generator instead of explicitly constructing list or tuple -- List comprehension for creating new lists -- Dictionary comprehension for creating dictionaries -- Merging dictionaries -- Swapping two variables -- Concatenating strings -- Using local variables -- 9.3.3 Utilizing tensor operations -- 9.3.4 Optimizing tensor indexing efficiency -- 9.4 Compiling Python code -- 9.4.1 Numba -- 9.4.1.1 Unrolling -- 9.4.1.2 Numba vectorization issues -- 9.4.2 Cython -- 9.4.3 Pythran -- 9.4.4 Comparison of Cython, Pythran, and Numba -- Ease of use -- Support for Python features -- Optimization hints -- Profiling -- NumPy specialization -- SIMD vectorization -- Compilation cost -- Execution speed -- 9.5 Optimization with compiled languages -- 9.5.1 GCC compiler -- 9.6 Optimization for I/O -- 9.6.1 Storage layout -- HDF5 format -- Memory mapping -- HDF5 vs memmap -- 9.6.2 Compressing data -- 9.6.3 Overlapping computation and I/O -- 9.7 Precomputation and memoization -- 9.7.1 LRU cache -- 9.7.2 Functional programming -- 9.7.3 Dynamic programming -- 9.8 Optimization with lazy evaluation -- Summary -- References -- 10 Parallel computation -- 10.1 Multithreading -- 10.1.1 The threading module -- How to manage data sharing between threads? ...”
Published 2025
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138by Sun, QimingTable of Contents: “...9.3.1 The dis module -- 9.3.2 Performance-friendly Python code -- Using enumerate for index and object iteration -- Using generator instead of explicitly constructing list or tuple -- List comprehension for creating new lists -- Dictionary comprehension for creating dictionaries -- Merging dictionaries -- Swapping two variables -- Concatenating strings -- Using local variables -- 9.3.3 Utilizing tensor operations -- 9.3.4 Optimizing tensor indexing efficiency -- 9.4 Compiling Python code -- 9.4.1 Numba -- 9.4.1.1 Unrolling -- 9.4.1.2 Numba vectorization issues -- 9.4.2 Cython -- 9.4.3 Pythran -- 9.4.4 Comparison of Cython, Pythran, and Numba -- Ease of use -- Support for Python features -- Optimization hints -- Profiling -- NumPy specialization -- SIMD vectorization -- Compilation cost -- Execution speed -- 9.5 Optimization with compiled languages -- 9.5.1 GCC compiler -- 9.6 Optimization for I/O -- 9.6.1 Storage layout -- HDF5 format -- Memory mapping -- HDF5 vs memmap -- 9.6.2 Compressing data -- 9.6.3 Overlapping computation and I/O -- 9.7 Precomputation and memoization -- 9.7.1 LRU cache -- 9.7.2 Functional programming -- 9.7.3 Dynamic programming -- 9.8 Optimization with lazy evaluation -- Summary -- References -- 10 Parallel computation -- 10.1 Multithreading -- 10.1.1 The threading module -- How to manage data sharing between threads? ...”
Published 2025
Connect to the full text of this electronic book
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