Cross-Cluster Accelerator Communication for ML Training Scalability
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Solution Overview
Problem
Existing systems face inefficiencies in resource allocation and communication overhead when training large-scale neural networks across multiple clusters of hardware accelerators, leading to suboptimal throughput and scalability in machine learning workloads.
Innovation Solution
Implementing cross-cluster communication techniques that optimize data center network communication by using one-to-one data exchange between corresponding hosts of hardware accelerator clusters, combined with data and model parallelism strategies to manage and distribute machine learning workloads efficiently across interconnected hardware accelerators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training is performed on a single cluster of hardware accelerators, then resource allocation is simpler, but training throughput is limited
Solution Approach 1:
The system divides hardware accelerators into multiple clusters (first cluster and second cluster) with corresponding hosts, allowing distributed training across separate computing units. This segmentation enables parallel processing of training workloads while maintaining manageable system architecture through hierarchical organization.
Solution Approach 2:
The patent implements a nested structure where hardware accelerators are organized within clusters, which are in turn organized within a broader computing system. The first and second clusters operate as nested units within the overall training system, enabling scalable resource allocation and coordinated operation across multiple hierarchical levels.
2Productivity
If multiple clusters of hardware accelerators are used, then training throughput increases, but communication overhead increases
Solution Approach 1:
Host computers serve as intermediary components between the first and second clusters of hardware accelerators. These hosts facilitate efficient communication and data exchange between clusters, managing the communication overhead through coordinated data transfer protocols and buffering mechanisms that minimize redundant transmissions.
Solution Approach 2:
The system performs preliminary data preparation and distribution to clusters before training begins. By pre-loading necessary datasets and model parameters to appropriate clusters, the system reduces communication overhead during actual training operations and enables more efficient parallel processing.
3Adaptability or versatility
If hardware accelerators are distributed across multiple clusters, then scalability improves, but resource allocation becomes more complex
Solution Approach 1:
The system implements dynamic resource allocation mechanisms that can adaptively assign hardware accelerators to different clusters based on workload requirements. This dynamic approach enables the system to scale flexibly while managing allocation complexity through automated scheduling algorithms that consider real-time system state and workload characteristics.
Solution Approach 2:
The host computers in the system serve multiple functions: they act as communication intermediaries, data buffers, and coordination nodes between clusters. This multi-functionality simplifies resource allocation management by consolidating control functions in universal components that can operate across different cluster configurations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads across hardware accelerators. One of the systems comprises a first plurality of hardware accelerators that are interconnected over a first network and one or more corresponding hosts for the first plurality of hardware accelerators; and a second plurality of hardware accelerators that are interconnected over a second network and one or more corresponding hosts for the second plurality of hardware accelerators, wherein the corresponding hosts for the first and second pluralities of hardware accelerators are connected over a third network. For example, the first and second network can each be a respective Inter-Core Interconnect (ICI) network, while the third network can be a data center network, e.g., an Ethernet network.