The efficiency issue of logic optimization becomes critical as the scale of VLSI designs grows. Since various algorithms are interleaved during optimization to ensure quality, it is necessary to accelerate those commonly used algorithms for obtaining substantial total speed-up. This paper proposes novel parallel algorithms for AIG refactoring and AND-balancing. Equipped with delicately designed parallel-friendly, data-race-free frameworks and GPU data structures, our algorithms obtain significant speed-up and enable the resyn2 sequence to be fully GPU-parallelized when combined with GPU rewriting. Experiments show that on large AIGs, we achieve average accelerations up to 45.9×over ABC with comparable or better qualities.