Data-parallel distributed training (DDT) is the de facto way to accelerate deep learning on multiple GPUs.In DDT, communication for gradient synchronization is the major efficiency bottleneck.Many gradient compression (GC) algorithms have been proposed to address this communication bottleneck by reducing the amount of communicated data.Unfortunately, it has been observed that GC only achieves moderate performance improvement in DDT, or even harms the performance.In this paper, we argue that the current way of deploying GC in a layer-wise fashion reduces communication time at the cost of non-negligible compression overheads.To address this problem, we propose Cupcake, a compression optimizer to fully unleash GC algorithms' advantages in accelerating DDT. It applies GC algorithms in a fusion fashion and determines the provably optimal fusion strategy to maximize the training throughput of compression-enabled DDT jobs.Experimental evaluations show that GC algorithms with Cupcake can achieve up to 2.03x speedup in training throughput over training without GC, and up to 1.79x speedup over the state-of-the-art approaches of applying GC to DDT in a layer-wise fashion.