The correctness of control software in many safety-critical applications such as autonomous vehicles is very crucial. One approach to achieve this goal is through "symbolic control", where complex physical systems are approximated by finite-state abstractions. Then, using those abstractions, provably-correct digital controllers are algorithmically synthesized for concrete systems, satisfying some complex high-level requirements. Unfortunately, the complexity of constructing such abstractions and synthesizing their controllers grows exponentially in the number of state variables in the system. This limits its applicability to simple physical systems. This paper presents a unified approach that utilizes sparsity of the interconnection structure in dynamical systems for both construction of finite abstractions and synthesis of symbolic controllers. In addition, parallel algorithms are proposed to target high-performance computing (HPC) platforms and Cloud-computing services. The results show remarkable reductions in computation times. In particular, we demonstrate the effectiveness of the proposed approach on a 7-dimensional model of a BMW 320i car by designing a controller to keep the car in the travel lane unless it is blocked.