Designing Safe NumPy Broadcasting for Vectorized Operations
A concise architecture note on using NumPy broadcasting safely: requirements, minimal design, trust boundaries, checks, failure modes, and when to reconsider the approach.
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Stop Looping: Optimizing MATLAB Performance via Vectorization
Learn how to replace slow for-loops in MATLAB with vectorization and implicit expansion to leverage optimized BLAS/LAPACK libraries for engineering computations.
matlabStop Looping Through Your Data: Mastering NumPy Boolean Indexing
Stop using for-loops to filter NumPy arrays. Learn how to use vectorized boolean indexing to create masks and extract data at C-speed.
Optimizing Numerical Throughput with Fortran Array Syntax
Use Fortran’s native array syntax to let the compiler emit SIMD instructions, boosting numerical throughput. Learn the design requirements, trust boundaries, verification steps, and failure modes for optimal performance.
Stop Looping: Speeding Up MATLAB Simulations with Vectorization
Stop slowing down MATLAB simulations with iterative loops. Learn how element-wise operators, logical indexing, and implicit expansion leverage SIMD performance, with a benchmarked example.
Choosing LLVM Loop Vectorization Settings on x86-64: A Decision Guide
A decision guide to LLVM loop vectorization on x86-64: compare default, forced, and pragma-based options, then validate with optimization remarks, disassembly, and benchmarks.
llvmNumPy Broadcasting: Trailing‑Axis Alignment Rules and Shape‑Safe Coding Practices
NumPy broadcasting aligns array dimensions from the trailing axis, enabling zero‑copy vectorized ops when shapes are compatible. This article explains the alignment rule, a (3,1) ⊕ (1,4) example, limitations with incompatible shapes, and operational checks you can add to your code.
Diagnosing Slow Fortran Array Operations: Contiguity, Temporaries, and Vectorization
A step‑by‑step diagnostic guide for Fortran array performance: detect temporaries, enforce contiguous layout, enable vectorization, and verify with assembly and perf counters.
MATLAB Vectorize vs Loop: What Modern Releases Actually Changed
The "always vectorize" advice is outdated. Modern MATLAB's JIT and implicit expansion make preallocated loops competitive. Profile first, then choose based on memory, readability, and your actual hardware.