Distributed Compute Nodes and Data Reformatting for Low-Latency Analytics
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Solution Overview
Problem
Conventional systems struggle to efficiently process and analyze large volumes of data due to overwhelming computational resources and increased latency, leading to prohibitively expensive or impractical solutions for high throughput data processing and analytics.
Innovation Solution
A distributed hardware architecture with compute nodes and a data processing system that reformats data structures and executes operations independently or cooperatively, utilizing programmable logic components and data movers to optimize data transfer and processing across multiple nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems use exhaustive computational approaches to process large datasets, then processing completeness is improved, but processing time and power consumption increase significantly
Solution Approach 1:
The system divides the data processing task into distributed operations across multiple compute nodes. Each node processes a segment of the data independently, then results are aggregated. This segmentation allows parallel processing that maintains completeness while reducing total processing time compared to sequential exhaustive approaches on a single system.
Solution Approach 2:
The patent introduces a distributed hardware architecture that adds a spatial dimension to processing. Instead of processing data sequentially through time on a single system, the system distributes computation across multiple nodes in space, enabling simultaneous processing of data segments and thereby reducing overall processing time while maintaining completeness.
2Productivity
If conventional systems increase computational resources to handle larger datasets, then processing capability is improved, but cost and complexity increase prohibitively
Solution Approach 1:
The compute nodes are designed as universal, multi-functional units that can perform various data processing operations. Each node contains programmable logic that can be configured for different algorithms and processing tasks. This universality allows the system to handle diverse data processing requirements using the same hardware architecture, improving productivity without proportionally increasing complexity.
Solution Approach 2:
The system dynamically adjusts processing parameters such as the number of active compute nodes, data partitioning strategy, and algorithm selection based on the specific dataset and processing requirements. This parameter adaptability allows the system to optimize productivity for different workloads without requiring proportional increases in hardware complexity or configuration complexity.
3Reliability
If data is processed in its original format, then data integrity is preserved, but processing efficiency decreases due to suboptimal data presentation for analysis
Solution Approach 1:
The system performs preliminary data reformatting operations before the data reaches the analytic algorithms. Compute nodes transform data into optimized formats that enhance processing efficiency, such as reorganizing data structures or converting data types. This preliminary action maintains data integrity through accurate transformation while significantly improving subsequent processing efficiency.
Solution Approach 2:
The distributed compute nodes act as intermediaries between the raw data and the analytic algorithms. These intermediate processing stages reformats data into presentation formats that are optimally suited for analysis, bridging the gap between raw data integrity requirements and algorithmic processing efficiency requirements.
Data Source
AI summary
A system and method employing a distributed hardware architecture, either independently or in cooperation with an attendant data structure, in connection with various data processing strategies and data analytics implementations are disclosed. A compute node may be implemented independent of a host compute system to manage and to execute data processing operations. Additionally, an unique algorithm architecture and processing system and method are also disclosed. Different types of nodes may be implemented, either independently or in cooperation with an attendant data structure, in connection with various data processing strategies and data analytics implementations.