Heterogeneous Computing Terminal Task Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Single edge computing processors have limited capabilities and are cost-ineffective for processing large amounts of data, struggling to meet low-latency requirements in computing-intensive applications.
Innovation Solution
A heterogeneous computing terminal with a processing unit and multiple computing units that dynamically assign tasks based on a generated task scheduling strategy, allowing for online upgrades of computing units to optimize processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single edge computing processor is used, then device complexity is reduced, but processing capability and productivity are limited
Solution Approach 1:
The system divides the computing processor into multiple independent computing units (CPU, GPU, NPU, etc.), each capable of independently processing different types of tasks. This segmentation allows the system to handle diverse computing workloads simultaneously, significantly improving processing capability while maintaining manageable complexity through modular design.
Solution Approach 2:
The heterogeneous computing units are designed to be universally applicable for different task types. Each computing unit can process multiple categories of tasks, and the task scheduling system can dynamically allocate tasks to appropriate units based on their capabilities, enhancing overall productivity without requiring specialized hardware for each task type.
2Productivity
If computing units are upgraded to enhance processing capability, then productivity improves, but device complexity and upgrade management become more difficult
Solution Approach 1:
The system implements dynamic task scheduling that can adapt to changing computing unit capabilities in real-time. When computing units are upgraded, the task scheduling system automatically detects the changes and redistributes tasks optimally, allowing the system to maintain high productivity without requiring manual reconfiguration or complex upgrade management procedures.
Solution Approach 2:
The task scheduling system automatically manages computing unit upgrades by detecting capability changes and self-adjusting task allocation. This self-service mechanism eliminates the need for manual intervention in upgrade management, reducing operational complexity while maintaining improved processing efficiency from upgraded units.
3Adaptability or versatility
If multiple computing units are introduced to handle complex tasks, then processing versatility improves, but task scheduling complexity increases
Solution Approach 1:
The task scheduling system incorporates feedback mechanisms that continuously monitor computing unit status, task progress, and system performance. Based on this feedback, the system dynamically adjusts task allocation to optimize versatility while managing scheduling complexity through data-driven decision-making rather than complex predefined rules.
Solution Approach 2:
The system manages task scheduling complexity by changing key parameters such as task priority, computing unit capacity weights, and allocation thresholds. These parameter adjustments allow the system to adapt to different workload scenarios and computing unit configurations without requiring complex scheduling algorithms, thereby maintaining processing versatility with manageable complexity.
Data Source
AI summary
A heterogeneous computing terminal for task scheduling is provided, including a processing unit and multiple computing units. The heterogeneous computing terminal generates a task scheduling strategy. The target computing unit acquires a target upgrade program, is upgrade based on the target upgrade program, processes a target task and sends a processing result to the processing unit. The processing unit combines processing results sent by multiple target computing units to obtain a processing result of an initial task set. The heterogeneous computing terminal can upgrade the computing unit online, that is, dynamically update the computing unit, thereby greatly expanding the versatility of the computing unit of hardware. Scheduling strategy adapted to the task can be generated, and the computing unit can dynamically provide better task processing capabilities. The computing efficiency of the heterogeneous computing terminal is improved. Further, processing capability and processing efficiency are better when facing complex tasks.