Q-adaptive: Multi-Agent Reinforcement Learning Routing on Dragonfly Network

Temel Kavramlar

Multi-agent reinforcement learning improves routing efficiency in Dragonfly networks.

Özet

This content discusses the implementation of Q-adaptive routing, a multi-agent reinforcement learning scheme for Dragonfly systems. It addresses the limitations of traditional adaptive routing algorithms by leveraging advanced machine learning technology. The study introduces a two-level Q-table design that enhances computational efficiency and memory usage compared to previous methods. Evaluation results demonstrate significant improvements in system throughput and packet latency, outperforming existing adaptive routing algorithms under various traffic patterns.
Abstract:

High-radix interconnects like Dragonfly rely on adaptive routing.
Current adaptive routing algorithms may lead to interconnect congestion due to inaccurate global path congestion estimation.
Q-adaptive routing is introduced as a multi-agent reinforcement learning scheme for Dragonfly systems.
It enables routers to autonomously learn to route by leveraging advanced reinforcement learning technology.
The proposed Q-adaptive routing achieves significant improvements in system throughput and packet latency compared to existing adaptive routing algorithms.
Introduction:

Interconnect network is crucial in high-performance computing systems.
Dragonfly networks offer high scalability and path diversity for efficient data exchange.
Adaptive routing dynamically delivers packets based on real-time network conditions.
Existing adaptive routing methods rely on local information, leading to potential network congestion.
Technical Challenges:

Topology uniqueness poses challenges in selecting optimal paths.
Routing livelock and deadlock must be avoided in large-scale systems like Dragonfly.
Distributed learning requires coordination among independent agents.
Implementation cost should be lightweight and scalable.
Q-adaptive Routing:

Introduces a two-level Q-table design for efficient decision-making.
Implements dynamic routing based on the two-level Q-table with bias control towards minimal paths.
Updates Q-values using hysteretic Q-learning approach for stability and fast convergence.
Evaluation:

Utilizes SST/Merlin simulator for performance evaluation.
Outperforms existing adaptive routing algorithms under different traffic patterns (UR, ADV+1, ADV+4).
Achieves higher system throughput and lower packet latency compared to traditional methods.

İstatistikler

Q-adaptive can achieve up to 88.25% system throughput under UR traffic pattern with 0.76 µs average packet latency.
Under ADV+1 pattern, it reaches 48.20% system throughput with an average of 3.06 hops per packet delivery at the offered load of 0.5.
For ADV+4 pattern, it achieves up to 44.93% system throughput with an average of 4.27 hops per packet delivery at the offered load of 0.5.

Alıntılar

"Q-adaptive outperforms all the adaptive routing algorithms regarding system throughput."
"Q-adaptive routes packets efficiently within five hops, solving livelock issues."
"Q-adaptive dynamically reroutes packets through intermediate groups when necessary."

Önemli Bilgiler Şuradan Elde Edildi

Q-adaptive

by Yao Kang,Xin... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16301.pdf

Daha Derin Sorular

How does the two-level Q-table design improve computational efficiency in Q-adaptive routing?

The two-level Q-table design improves computational efficiency in Q-adaptive routing by reducing memory usage and addressing the issue of outdated Q-values. Compared to the original single-level Q-table, the two-level Q-table is smaller in size, requiring only half the memory space. This reduction in memory usage makes it more scalable for large-scale systems like Dragonfly networks. Additionally, having separate tables for source and destination information allows routers to have more learning information and mitigates the problem of outdated values due to sparse updates.

What are the implications of avoiding livelock and deadlock in large-scale interconnect networks like Dragonfly?

Avoiding livelock and deadlock in large-scale interconnect networks like Dragonfly is crucial for ensuring efficient packet delivery and network stability. Livelock occurs when packets continuously circulate without making progress towards their destinations, leading to wasted bandwidth and potential congestion. Deadlock can occur when packets get stuck due to channel dependencies or resource conflicts, halting network operations.
By preventing livelock, packets are guaranteed to reach their destinations within a limited number of hops, maintaining efficient routing paths. Avoiding deadlock ensures that packets can traverse through the network without getting stuck or causing bottlenecks at certain points. Overall, eliminating livelock and deadlock enhances system performance, reduces latency, and improves overall network throughput on large-scale interconnect networks.

How can distributed learning challenges be addressed effectively in multi-agent reinforcement learning schemes?

Distributed learning challenges in multi-agent reinforcement learning schemes can be effectively addressed through various strategies:

Independent Learning: Allowing agents to update their policies independently based on local observations helps prevent interference between agents' decisions.

Coordinated Updates: Implementing coordinated updates where agents share information periodically or under specific conditions can enhance convergence speed while maintaining stability.

Hysteretic Learning: Utilizing hysteretic learning with separate positive and negative learning rates enables stable policy updates even with changing environments.

Learning Rates Tuning: Adjusting positive (𝛼) and negative (𝛽) learning rates based on system dynamics helps control how quickly agents adapt their policies without destabilizing overall performance.

Shared Knowledge Base: Establishing a shared knowledge base where agents exchange learned insights or strategies can promote collaboration while preserving individual decision-making autonomy.

By incorporating these approaches into multi-agent reinforcement learning frameworks like MARL routing algorithms such as Q-adaptive, distributed learning challenges can be effectively managed for improved system performance and scalability across interconnected networks like Dragonfly topologies.