GPGPU Interconnect Communication in Distributed Machine Learning
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Solution Overview
Problem
Current systems for parallel graphics data processing, particularly in the context of neural network models and general-purpose graphics processing units (GPGPUs), face inefficiencies in communication and resource management, especially in distributed machine learning environments.
Innovation Solution
Implementing communication optimizations and resource management techniques for distributed machine learning using GPGPUs, including topology-aware algorithms for flexible node grouping and quality of service (QoS) management between parallel processor compute nodes, along with server-less accelerator pooling for inference operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed machine learning using GPGPUs is implemented, then processing capability and scalability are improved, but communication overhead and resource management complexity increase
Solution Approach 1:
The patent introduces a communication manager as an intermediary component that handles communication between compute nodes. This manager abstracts and simplifies the complex communication protocols and resource management tasks, allowing GPGPUs to work in distributed configurations without directly managing the complexity of inter-node communication. The communication manager mediates data transfer, synchronization, and resource allocation across the distributed system.
Solution Approach 2:
The system is segmented into distinct functional components: compute nodes containing GPGPUs for processing, separate communication managers for handling data transfer, and storage systems for data persistence. This segmentation allows each component to be optimized independently and simplifies the overall resource management by distributing responsibilities across multiple specialized units rather than requiring centralized control of all resources.
2Speed
If communication optimizations are implemented, then communication efficiency between compute nodes is improved, but system complexity increases
Solution Approach 1:
The communication manager performs preliminary actions by pre-configuring communication channels, pre-fetching data, and pre-establishing optimization parameters before actual compute operations begin. This preliminary setup enables faster communication during execution without requiring complex real-time decision-making, thereby improving communication efficiency while keeping the runtime system complexity manageable.
3Productivity
If topology-aware algorithms for flexible node grouping are used, then processing efficiency is improved, but algorithm complexity increases
Solution Approach 1:
The topology-aware algorithms utilize parameter changes by dynamically adjusting grouping parameters based on the physical or logical topology of the compute nodes. Instead of implementing complex topological analysis from scratch, the system changes parameters such as node grouping configurations, data placement strategies, and communication routing based on detected topology characteristics. This approach achieves processing efficiency improvements through parameter optimization rather than through inherently complex algorithmic logic.
Data Source
AI summary
Embodiments described herein provide an apparatus comprising an interconnect switch configured to couple with a plurality of graphics processors via a plurality of point-to-point interconnects and one or more processors including a graphics processor coupled with the interconnect switch via a point-to-point interconnect of the plurality of point-to-point interconnects.