Distributed Algorithm Execution in Information Processing Systems
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Solution Overview
Problem
In information processing systems with multiple devices, the efficiency of data processing is often inadequate due to the intensive time and resource requirements for a single server to collect and compute data from numerous devices, limiting the ability of non-server devices to perform computations.
Innovation Solution
An information processing device equipped with a data receiving unit, computation execution unit, and data transmitting unit, which receives and executes algorithms, transmits algorithm data to other devices, and determines computational results in a branched manner based on factors like the number of devices traversed, time of receipt, and specific device traversal, optimizing the processing efficiency across multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single server collects and computes data from all information processing devices, then centralized computation can be performed, but the processing time and resource consumption become excessively high
Solution Approach 1:
The patent segments the centralized computation task into multiple distributed computation tasks. Instead of one server collecting all data and performing computations centrally, the computation is divided and distributed to multiple information processing devices. Each device executes computations locally on its stored data using received algorithm data, thereby parallelizing the computation process and significantly reducing total processing time.
Solution Approach 2:
The patent transitions from a single-dimension centralized processing model to a multi-dimension distributed processing model. By introducing spatial distribution across multiple devices and organizing computations in a tree-structured hierarchy with multiple levels and branches, the system achieves parallel processing across different dimensional spaces, dramatically improving computation efficiency.
2Adaptability or versatility
If a single server performs all computations, then centralized control is maintained, but non-server devices cannot perform computations
Solution Approach 1:
The patent makes information processing devices universal by enabling them to perform multiple functions. Devices can act as both data storage units and computation execution units. The same device type can participate in different roles within the tree structure (as internal nodes receiving algorithm data and as leaf nodes providing data), allowing any device to potentially perform computations depending on its position and capabilities in the hierarchy.
Solution Approach 2:
The patent introduces dynamic role assignment based on device capabilities and positions in the tree structure. Devices dynamically adapt their functionality based on whether they are internal nodes or leaf nodes, and whether they have sufficient computational resources. The system dynamically adjusts the computation distribution according to actual device performance and data characteristics.
3Productivity
If algorithm data is transmitted to multiple devices in a branched manner, then computation is distributed and efficiency improves, but determining and comparing computational results from multiple branches becomes complex
Solution Approach 1:
The patent applies preliminary action by having internal nodes perform preliminary determination of computational results before final aggregation. Each internal node receives computational results from its child nodes, performs preliminary processing and validation, and determines whether to accept or reject these results based on predetermined criteria. This preliminary action simplifies the final result determination at the root node by filtering and validating results along the computation path.
4Productivity
If a tree structure is used for distributing algorithm data, then scalable distributed processing is achieved, but the system requires coordination between multiple devices
Solution Approach 1:
The patent uses preliminary action by pre-establishing the tree structure and communication paths before computation begins. Algorithm data is transmitted along predetermined paths from root to leaf nodes, and results are aggregated along predetermined paths from leaves to root. This preliminary structuring of data flow and communication routes minimizes coordination overhead during actual computation by eliminating the need for dynamic route discovery and negotiation.
Data Source
AI summary
An information processing device is provided with a data receiving unit having a function of receiving first algorithm data that is data stating a first algorithm from a first information processing device, a computation execution unit having a function of executing computations based on the first algorithm stated in the first algorithm data received by the data receiving unit and using data stored in a first storage unit in the computations on a basis of the first algorithm data and the data stored in the first storage unit, and a data transmitting unit having a function of transmitting second algorithm data that is data stating a second algorithm according to the first algorithm to a second information processing device.