- Introduced the `ParallelUnbalancedWork` class to efficiently execute parallel loops over a range of integers, handling unbalanced workloads.
- Added static `For` methods to support parallel execution with and without thread-local data, including initialization and finalization functions.
- Utilized thread pooling and a shared counter (`SharedCounter`) to distribute iterations among threads dynamically.
- Implemented internal classes (`BaseData`, `Data`, and `InitProcessor<TLocal>`) to manage shared state and thread synchronization.
- Aimed to optimize performance in scenarios where the workload per iteration is uneven, ensuring better resource utilization and reduced execution time.