pFaces: an acceleration ecosystem for symbolic control. The correctness of control software in many safety-critical applications such as autonomous vehicles is crucial. One technique to achieve correct control software is called ”symbolic control”, where complex systems are approximated by finite-state abstractions. Then, using those abstractions, provably-correct digital controllers are algorithmically synthesized for concrete systems, satisfying complex high-level requirements. Unfortunately, the complexity of synthesizing such controllers grows exponentially in the number of state variables. However, if distributed implementations are considered, high-performance computing platforms can be leveraged to mitigate the effects of the state-explosion problem. We propose pFaces, an extensible software-ecosystem, to accelerate symbolic control techniques. It facilitates designing parallel algorithms and supervises their executions to utilize available computing resources. To demonstrate its capabilities, novel parallel algorithms are designed for abstraction-based controller synthesis. Then, they are implemented inside pFaces and dispatched, for parallel execution, in different heterogeneous computing platforms, including CPUs, GPUs and Hardware Accelerators (HWAs). Results show remarkable reduction in the computation time by several orders of magnitudes as number of processing elements (PEs) increases, which easily outperforms all the existing tools.