We present MegaScale-MoE, a production system tailored for the efficient training of large-scale mixture-of-experts (MoE) models. MoE emerges as a promising architecture to scale large language models (LLMs) to unprecedented sizes, thereby enhancing model performance. However, existing MoE training systems experience a degradation in training efficiency, exacerbated by the escalating scale of MoE models and the continuous evolution of hardware.

As LLM training grows increasingly resource-intensive and expert parallelism (EP) becomes essential for scaling MoE models, EP optimizations are widely adopted in production frameworks like Megatron-LM. Existing solutions often rely on ad-hoc, complex kernels that lack adaptability across diverse optimization configurations and frequently neglect numerical stability, failing to meet the strict precision requirements of large-scale training.

Convolutional neural networks (CNNs) have achieved remarkable success in various application fields. Although model compression techniques mitigate the ever-increasing resource demands of large CNN models, the compressed models usually exhibit irregular memory access and unstructured sparsity, which are difficult for dominant operators such as sparse convolution to achieve expected performance speedup on popular inference platforms such as GPU. In this paper, we propose Tetris, an efficient sparse convolution approach optimized for GPU. Tetris first fully exploits the input reuse opportunity of sparse convolution to reduce the memory accesses to global memory. It then adopts a stride packed filter (SPF) format and a bank-sensing reorganization scheme to eliminate the irregular memory accesses caused by unstructured sparsity. It also leverages a filter group reorder technique to address load imbalance among threads, and a parameter tuning method to determine the optimal parameters of the sparse convolution implementation. The experiment results show that Tetris outperforms dense/sparse convolution libraries and cutting-edge implementations with promising performance speedup.