Collective Offload Chunk Routing for Scalable Network Operations
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Solution Overview
Problem
Collective communications in large-scale networks face scalability issues due to increased processing demands, leading to inefficiencies in data transfer and processing complexity.
Innovation Solution
Implementing a collective engine that manages network collective offloading by performing operations on datasets, splitting results into chunks, and routing them through optimized network topologies to ensure efficient data transfer and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If collective communications are performed in large-scale networks, then global communications operations can be achieved amongst all processes/nodes, but scalability issues arise due to increased processing demands
Solution Approach 1:
The patent segments collective communication operations into multiple chunks, where each chunk is processed and transmitted separately through the network. This divides the large-scale communication task into smaller, more manageable units that can be handled by individual nodes without overwhelming processing demands, thereby maintaining scalability while achieving global communications.
Solution Approach 2:
The patent introduces a collective engine as an intermediary component that manages and coordinates chunked communication operations. This intermediary handles the complexity of routing and managing multiple chunks, shielding individual nodes from the full processing burden while enabling global communications across the network.
2Power
If processing power is increased to handle collective communications, then communication operations can be performed, but scalability limits are reached
Solution Approach 1:
By segmenting communication data into chunks, the patent enables parallel processing across multiple nodes simultaneously. Each node processes only its assigned chunk rather than handling entire communication operations, which distributes processing power requirements and improves overall system scalability without sacrificing communication capability.
Solution Approach 2:
The patent implements partial action by having each node process only a portion (chunk) of the total communication data rather than requiring full processing capability at each node. This partial processing approach allows the system to scale by adding more nodes, as each new node contributes to handling a fraction of the total communication load.
3Productivity
If data is transferred in chunks through optimized network topologies, then data transfer efficiency is enhanced, but additional routing management complexity is introduced
Solution Approach 1:
The collective engine is designed as a universal management component that handles multiple functions including chunk routing, topology optimization, and destination matching. This multi-functional intermediary simplifies routing management by consolidating complexity into a single system that can adapt to different network topologies and communication patterns.
Solution Approach 2:
The patent implements feedback mechanisms where the collective engine monitors chunk transmission and routing effectiveness, then adjusts routing strategies accordingly. This feedback loop enables the system to optimize data transfer efficiency while managing routing complexity dynamically, adapting to network conditions without requiring static complex routing configurations.
Data Source
AI summary
The disclosed device can perform a collective operation on received datasets, and split the result into chunks in accordance with a chunking scheme. The device can also forward the chunks in accordance with a routing scheme that can direct chunks to appropriate nodes of a collective network. Various other methods, systems, and computer-readable media are also disclosed.