Snaking Dimension-Ordered Node Numbering for Multiprocessor Placement
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Solution Overview
Problem
In multiprocessor computer systems, efficiently managing and scheduling resources across multiple nodes is challenging, especially in environments with multiple applications and operating systems, as traditional node numbering methods do not account for the multi-dimensional nature of processor interconnect networks, leading to inefficient node placement and performance variations.
Innovation Solution
Implementing a snaking dimension-ordered node numbering system that traverses smaller dimensions first and snakes through larger dimensions, combined with localized clustering, to prioritize node placement near each other, thereby reducing network distance and congestion, and enhancing resource utilization and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional node numbering methods are used in multiprocessor systems, then the system is simpler to implement, but node placement efficiency deteriorates and network congestion increases
Solution Approach 1:
The patent applies dimensionality change by transitioning from traditional linear node numbering to a multi-dimensional coordinate system (x, y, z dimensions). Each node is assigned coordinates that reflect its physical position in the processor array, enabling the system to exploit spatial relationships and improve data locality without fundamentally changing the hardware architecture.
Solution Approach 2:
The patent changes the numbering parameter from simple sequential integers to multi-dimensional coordinates with specific ordering rules. The snaking dimension-ordered numbering scheme transforms how nodes are indexed by considering multiple spatial dimensions and alternating traversal directions, which optimizes memory access patterns and reduces network congestion.
2Speed
If nodes are placed far apart in the network, then load balancing is improved, but communication time between nodes increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing the mapping between application indices and node coordinates before application execution. The system calculates optimal node placements in advance based on the snaking dimension-ordered numbering scheme, so that during runtime, applications can be quickly mapped to nearby nodes without real-time optimization overhead.
Solution Approach 2:
The patent introduces an intermediary mapping layer between the logical application space and the physical node space. The snaking dimension-ordered numbering system acts as this intermediary, translating application indices into optimized node coordinates that consider spatial proximity, thereby reducing communication time without requiring direct optimization during execution.
3Productivity
If applications are placed on non-contiguous nodes, then resource utilization is improved, but network congestion increases
Solution Approach 1:
The patent applies local quality by ensuring that applications accessing similar data are placed on nodes with similar coordinate values, creating local clusters of related computational tasks. This localization strategy maintains high resource utilization while minimizing the physical distance data must travel across the network, thereby reducing congestion.
Solution Approach 2:
The patent uses multi-dimensional coordinate space to organize nodes, allowing applications to be distributed across different dimensions rather than scattered randomly. The snaking ordering pattern ensures that contiguous indices in the logical space map to spatially proximate nodes in the physical space, maintaining locality despite distributed placement.
Data Source
AI summary
A multiprocessor computer system comprises a plurality of nodes, wherein the nodes are ordered using a snaking dimension-ordered numbering. An application placement module is operable to place an application in nodes with preference given to nodes ordered near one another.