EPLB

An open-source algorithm for expert parallelism load balancing, designed to optimize expert allocation and load balancing in multi-GPU environments.

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Expert Parallelism Load Balancer (EPLB) is a load balancing algorithm for Expert Parallelism (EP) in deep learning. It ensures load balance across different GPUs through a redundant expert strategy and a heuristic packing algorithm, while utilizing group-constrained expert routing to reduce inter-node data traffic. This algorithm is significant for large-scale distributed training, improving resource utilization and training efficiency.

Supports both hierarchical and global load balancing strategies to adapt to training needs at different stages.
Dynamically replicates experts with heavier loads through a redundant expert strategy to ensure load balance.
Utilizes group-constrained expert routing to place experts from the same group on the same node whenever possible
reducing cross-node communication.
Provides expert replication and placement plans based on estimated expert load
supporting custom load prediction methods.
Open-source implementation
facilitating easy integration and extension in different frameworks.

This product is designed for deep learning researchers and engineers who need to conduct large-scale distributed training
especially those using Expert Parallelism (EP) techniques. It helps optimize resource allocation
improve training efficiency
and reduce hardware costs.

In natural language processing (NLP) tasks
using EPLB to optimize the expert parallel training of Transformer models significantly improves training speed.
In computer vision tasks
EPLB achieves expert load balancing in multi-GPU environments
enhancing model performance.

1. Clone the EPLB repository to your local machine.
2. Install dependent libraries
such as PyTorch.
3. Prepare expert load data
for example

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