Graph Convolutional Networks (GCNs) are widely used in various domains. However, training distributed full-batch GCNs on large-scale graphs poses challenges due to inefficient memory access patterns and high communication overhead. This paper presents general and efficient aggregation operators designed for irregular memory access patterns. Additionally, we employ a pre-post-aggregation approach and a quantization with label propagation method to reduce communication costs. Combining these techniques, we develop an efficient and scalable distributed GCN training framework, \emph{SuperGNN}, for CPU-powered supercomputers. Experimental results on multiple large graph datasets show that our method achieves a speedup of up to 6$\times$ compared with the state-of-the-art implementations, and scales to 1000s of HPC-grade CPUs, without sacrificing model convergence and accuracy. Our framework achieves performance on CPU-powered supercomputers comparable to that of GPU-powered supercomputers, with a fraction of the cost and power budget.