To exploit Armadillo’s functions for parallel processing, follow these steps:
1. Utilize the OpenMP library: Armadillo supports OpenMP, which enables parallelism on multi-core systems. Ensure your compiler supports OpenMP and enable it during compilation.
2. Use element-wise operations: Armadillo automatically parallelizes element-wise operations like addition, subtraction, multiplication, and division. Replace loops with vectorized operations to leverage this feature.
3. Employ parallelized functions: Some Armadillo functions, such as “accu” and “transform,” are already parallelized. Use these built-in functions when possible.
4. Customize parallelization: For specific tasks not covered by built-in functions, use OpenMP directives (e.g., “#pragma omp parallel”) to manually parallelize code sections.
5. Optimize memory access: Minimize cache misses by accessing data in a contiguous manner. Use Armadillo’s submatrix views or advanced constructors to create non-contiguous submatrices without copying data.
6. Balance workload: Distribute work evenly among threads to avoid bottlenecks. Adjust the number of threads used by OpenMP if necessary.
