Reconfigurable Compute Nodes via Optical Circuit Switching
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Solution Overview
Problem
Current cloud computing data center architecture is inflexible, with fixed compute node configurations that are inefficient for applications requiring different numbers of GPUs, and aggregated functional elements that make upgrading or replacing individual components difficult, leading to resource underutilization and increased maintenance costs.
Innovation Solution
Implementing a reconfigurable computing cluster with an optical circuit switch and bidirectional fiber optic communications paths, allowing for dynamic reconfiguration of compute nodes by coupling appropriate resources and enabling upgrading of individual components without affecting others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If compute nodes use fixed configuration with aggregated functional elements, then device complexity is reduced and ease of manufacture is improved, but adaptability and resource utilization efficiency deteriorate
Solution Approach 1:
The patent segments compute nodes into separate functional components (CPUs, GPUs, storage, memory) that can be independently selected and configured. This allows the system to move from fixed aggregated modules to modular building blocks that can be assembled in different configurations based on application requirements, thereby improving adaptability while maintaining manufacturing simplicity through standardized components.
Solution Approach 2:
The patent implements dynamic reconfiguration capabilities where compute node compositions can be changed at runtime. The system allows adding or removing functional elements (such as GPUs or storage devices) from compute nodes based on workload demands, transforming the static fixed configuration into a dynamic adaptable structure that optimizes resource utilization.
2Device complexity
If compute nodes use fixed configuration, then device complexity is reduced, but productivity and resource utilization efficiency worsen
Solution Approach 1:
The patent creates universal compute node templates that can serve multiple application types. By defining standardized functional elements (CPU assets, GPU assets, storage assets, memory assets) that can be combined in various configurations, the system achieves multi-functionality where the same pool of resources can be dynamically allocated to different workloads, improving productivity without increasing individual node complexity.
Solution Approach 2:
The patent enables parameter changes in compute node configurations by allowing dynamic adjustment of the number and type of functional elements. The system can modify compute node parameters such as GPU count, storage capacity, and memory size based on application requirements, thereby improving productivity while maintaining manageable complexity through controlled parameter variation.
3Ease of manufacture
If functional elements are aggregated in fixed modules, then ease of manufacture is improved, but ease of repair and upgrading deteriorate
Solution Approach 1:
The patent segments compute nodes into independent replaceable functional elements (CPUs, GPUs, storage devices, memory modules). This segmentation allows individual components to be upgraded or repaired without replacing the entire compute node, improving ease of repair and maintenance while maintaining manufacturing simplicity through standardized modular components with defined interfaces.
4Reliability
If compute nodes are dedicated to specific applications, then reliability is improved, but adaptability and resource utilization worsen
Solution Approach 1:
The patent implements dynamic resource allocation where compute node assignments can be changed based on workload demands. The system allows compute nodes to be dynamically assigned to different applications or workloads, providing both reliability through dedicated assignment when needed and adaptability through flexible reassignment, thereby resolving the contradiction between dedicated and shared resource models.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for flexible compute node configurations, efficient resource utilization, and reduced maintenance costs by enabling the creation of compute nodes with varying GPU counts and allowing for component upgrades without replacing the entire module.
Implementation Method 1
a first plurality of computing assets, each of the first plurality of computing assets connected to the optical circuit switch by two or more bidirectional fiber optic communications paths
Data Source
AI summary
Reconfigurable computing clusters, compute nodes within reconfigurable computing clusters, and methods of operating a reconfigurable computing cluster are disclosed. A reconfigurable computing cluster includes an optical circuit switch, and a plurality of computing assets, each of the plurality of computing assets connected to the optical circuit switch by two or more bidirectional fiber optic communications paths.