Memory-Aware Load Balancing for Heterogeneous LLM Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Heterogeneous computing systems face inefficiencies due to varying hardware capabilities across nodes, leading to unequal processing speeds and inefficient data sharing, particularly in Large Language Model (LLM) training, where nodes with higher capabilities are underutilized and those with lower capabilities are overburdened, and data transfer via Remote Direct Memory Access (RDMA) is slow and costly.
Innovation Solution
Implement hardware-aware scheduling and leverage cache-coherent interconnect memories like Compute Express Link (CXL) to optimize node operations, using a memory pool accessible to all nodes, and minimize data transfer bottlenecks by allowing direct access to a common memory pool through cache-coherent interconnect protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware-aware scheduling is implemented in heterogeneous computing systems, then system efficiency and workload distribution are improved, but device complexity and scheduling overhead increase
Solution Approach 1:
The system performs preliminary characterization of each node's hardware capabilities (compute power, memory size, bandwidth) before scheduling tasks. This advance knowledge allows the scheduler to make informed decisions about workload distribution, matching tasks to appropriately capable nodes without requiring complex real-time adjustments during execution.
Solution Approach 2:
The scheduling system assigns different workload characteristics to different nodes based on their specific hardware capabilities. High-capability nodes receive more computationally intensive tasks, while lower-capability nodes handle lighter workloads. This localized optimization ensures each node operates at its optimal performance level, improving overall system efficiency.
2Ease of operation
If data is transferred via RDMA in heterogeneous systems, then data sharing between nodes is enabled, but transfer speed is slow and cost is high
Solution Approach 1:
The system merges the memory spaces of multiple nodes into a unified addressable memory pool through cache-coherent interconnect technology. This allows processing elements across different nodes to access data as if it were locally cached, eliminating the need for slow RDMA transfers while maintaining data sharing capabilities. The memory pool appears as a single coherent space to all processing elements.
Solution Approach 2:
The cache-coherent interconnect acts as an intermediary between nodes, providing a high-speed data exchange mechanism that replaces traditional RDMA. This intermediary layer abstracts the complexity of inter-node communication and provides cache-coherent access to the memory pool, significantly improving data transfer speeds while maintaining ease of operation.
3Ease of manufacture
If homogeneous hardware assumptions are used in training systems, then system design is simplified, but heterogeneous nodes operate inefficiently with underutilized high-capability nodes and overburdened low-capability nodes
Solution Approach 1:
The system dynamically adapts task assignment based on the actual heterogeneous capabilities of each node. Rather than assuming all nodes are identical, the scheduler continuously monitors and adjusts workload distribution to match each node's specific compute power, memory capacity, and bandwidth characteristics. This dynamic approach maximizes training efficiency while maintaining manageable system design through automated adaptation.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A heterogeneous computing system is described. The heterogeneous computing system may include nodes (105), which may include processing elements (135), the processing elements (135) including local memories (140). The nodes (105) may have capabilities that may differ. The heterogeneous computing system may also include a memory pool (145), accessible to the processing elements (135).