EPLB (Expert Parallelism Load Balancer)
EPLB is a load balancer for Mixture of Experts (MoE) models that optimizes expert placement and replication to balance computational load across GPUs.
Advanced Load Balancing
Key Features of EPLB
EPLB combines intelligent expert replication and placement strategies to optimize the performance of Mixture of Experts models.
System Design
EPLB Architecture
EPLB employs two main strategies for load balancing: hierarchical and global, each optimized for different deployment scenarios.
Hierarchical Load Balancing
Used when the number of nodes can be evenly divided by the number of expert groups. This strategy first distributes expert groups evenly across nodes, then replicates experts within each node, and finally packs the replicated experts onto GPUs.
Global Load Balancing
Used in other scenarios, this strategy ignores expert groups and directly replicates experts globally based on their computational load, then packs them onto GPUs to achieve balanced workload distribution.
Benchmarks
EPLB Performance
EPLB significantly improves the performance of MoE models by balancing expert workloads across GPUs.
Load Imbalance Reduction
Up to 85%
Reduction in computational load imbalance across GPUs
Throughput Improvement
Up to 40%
Increase in overall system throughput
GPU Utilization
95%+
Average GPU utilization with balanced expert placement
Scaling Efficiency
Near-linear
Scaling efficiency with increasing number of GPUs
Applications
EPLB Use Cases
EPLB is optimized for various deployment scenarios of Mixture of Experts models.
Frequently Asked Questions
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- What is EPLB?
- EPLB (Expert Parallelism Load Balancer) is a tool for optimizing the deployment of Mixture of Experts (MoE) models by balancing expert workloads across GPUs through intelligent expert replication and placement.
- How does EPLB work?
- EPLB works by analyzing the estimated computational load of each expert, determining how many replicas each expert needs, and then placing these replicas across GPUs to achieve balanced workload distribution. It offers two strategies: hierarchical load balancing and global load balancing.
- What is the difference between hierarchical and global load balancing?
- Hierarchical load balancing first distributes expert groups evenly across nodes, then replicates experts within each node. It's used when the number of nodes can be evenly divided by the number of expert groups. Global load balancing ignores expert groups and directly replicates experts globally based on their computational load.
- Why is load balancing important for MoE models?
- In MoE models, different experts may have vastly different computational loads. Without load balancing, some GPUs might be overloaded while others are underutilized, creating bottlenecks and reducing overall system throughput.
- How does EPLB reduce inter-node traffic?
- EPLB's hierarchical load balancing strategy places experts from the same group on the same node whenever possible, reducing the need for data transfer between nodes during inference or training.
- Is EPLB open source?
- Yes, EPLB is available as an open-source project on GitHub at https://github.com/deepseek-ai/EPLB. It is developed by DeepSeek AI to support efficient deployment of MoE models.