The increasing core count in modern processors offers substantial potential to accelerate parallel applications. However, this also introduces key challenges: rising overhead from work distribution and diminishing per-thread workloads can significantly degrade performance. To address this, developers can exploit parallelism at multiple levels or execute multiple applications simultaneously—though this approach demands enhanced support in parallel programming models to prevent resource oversubscription. In this work, we evaluate how different programming models leverage composable parallelism to improve efficiency, focusing on OpenMP and oneTBB in the context of the Breadth-First Search (BFS) algorithm. Our experimental analysis targets Arm-based architectures, assessing scalability and overhead trade-offs.