FUSCO: Faster Data Shuffling for MoE Models
Analysis
This paper addresses a critical bottleneck in training and inference of large Mixture-of-Experts (MoE) models: inefficient data shuffling. Existing communication libraries struggle with the expert-major data layout inherent in MoE, leading to significant overhead. FUSCO offers a novel solution by fusing data transformation and communication, creating a pipelined engine that efficiently shuffles data along the communication path. This is significant because it directly tackles a performance limitation in a rapidly growing area of AI research (MoE models). The performance improvements demonstrated over existing solutions are substantial, making FUSCO a potentially important contribution to the field.
Key Takeaways
- •FUSCO is a new communication library designed for efficient data shuffling in Mixture-of-Experts (MoE) models.
- •It addresses the performance bottleneck caused by inefficient data shuffling in existing communication libraries.
- •FUSCO achieves significant speedups over existing solutions by fusing data transformation and communication.
- •The library reduces training and inference latency in MoE tasks.
“FUSCO achieves up to 3.84x and 2.01x speedups over NCCL and DeepEP (the state-of-the-art MoE communication library), respectively.”