Abstract Binary Decision Diagram (BDD) construction and manipulation is an important part of CAD tasks. One of the ways to improve BDD package performance is to perform certain BDD operations in parallel with the GPU. The recent development of GPU frameworks for general-purpose programming, such as OpenCL or Nvidia CUDA, has made GPUs a very powerful and attractive option for developing... Binary Decision Diagram (BDD) construction and manipulation is an important part of CAD tasks. One of the ways to improve BDD package performance is to perform certain BDD operations in parallel with the GPU. The recent development of GPU frameworks for general-purpose programming, such as OpenCL or Nvidia CUDA, has made GPUs a very powerful and attractive option for developing high-performance numerical applications. This paper proposes an efficient implementation of the BDD package that distributes computational workloads over CPUs and GPUs. This implementation takes advantage of various parallelism sources found in the BDD package. The experimental results demonstrate that implementing this solution results in significant computational speedups. Read More Read Less