To optimize Armadillo-based code performance, consider the following techniques:
1. Exploit expressions: Armadillo’s expression templates enable lazy evaluation and automatic loop fusion, reducing memory usage and improving speed. Use compound expressions instead of multiple individual operations.
2. Utilize vectorization: Armadillo supports SIMD (Single Instruction Multiple Data) vectorization for element-wise operations on vectors and matrices. Ensure your compiler has auto-vectorization enabled or use explicit SIMD intrinsics.
3. Prefer inplace operations: Inplace functions like “+=” and “-=” reduce temporary object creation, lowering memory overhead. For example, use “A += B” instead of “A = A + B”.
4. Opt for subviews: When working with matrix subsets, use submatrix views (e.g., “A.submat()”) to avoid creating unnecessary copies.
5. Batch small matrix multiplications: Combine several smaller matrix multiplications into a single larger one to exploit cache locality and improve efficiency.
6. Link with optimized libraries: Armadillo relies on external BLAS/LAPACK libraries for linear algebra operations. Link against optimized versions like OpenBLAS or Intel MKL for better performance.
