Overview
DeepEP is a high-performance, low-latency communication library specifically engineered for Mixture-of-Experts (MoE) and expert parallelism (EP) workloads. It delivers optimized all-to-all GPU kernels for MoE dispatch and combine operations, supporting crucial features like low-precision (FP8) computation and seamless integration with modern GPU architectures.
Key Features
- High-Throughput Expert Parallelism: Offers specialized, efficient kernels for MoE dispatch and combine, enabling rapid data distribution and aggregation across experts.
- Optimized Domain Forwarding: Features kernels tuned for asymmetric bandwidth forwarding, such as NVLink to RDMA transfers, crucial for group-limited gating algorithms and large-scale distributed training.
- Low-Latency Inference Decoding: Provides a dedicated set of low-latency kernels utilizing pure RDMA for inference decoding, minimizing communication overhead.
- Communication-Computation Overlap: Introduces a hook-based mechanism for overlapping communication with computation, without occupying valuable SM resources, significantly boosting efficiency.
- Low-Precision Support: Natively supports low-precision operations, including FP8, for enhanced memory efficiency and computational speed.
- Flexible Network Configuration: Designed for InfiniBand, with theoretical compatibility for RoCE, and supports traffic isolation via Virtual Lanes.
Typical Use Cases
- Large-Scale Model Training: Accelerates training of massive MoE models by optimizing the communication patterns inherent in expert parallelism.
- Inference Prefilling: Enhances the performance of the prefilling stage in inference workloads where MoE dispatch is critical.
- Latency-Sensitive Inference Decoding: Minimizes end-to-end latency for decoding in MoE-based inference systems through its specialized low-latency kernels.
- Distributed Deep Learning Research: Serves as a foundational communication library for researchers exploring new MoE architectures and distributed training strategies.
