Parallel Processing Node Allocation Overhead Calibration
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Solution Overview
Problem
Efficient and optimal resource allocation during parallel processing is challenging due to overhead costs and complexity in managing multiple processing resources, leading to sub-optimal implementation that limits computing efficiency and user satisfaction.
Innovation Solution
A system that includes a sampler to measure task performance across different numbers of nodes and a calibrator to calculate an overhead factor, determining the optimal number of nodes for parallel execution by analyzing performance changes, and a resource scheduler to assign the optimal number of nodes for efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If more processing resources are allocated to parallel processing, then processing speed is improved, but overhead costs increase
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring task performance metrics and adjusting the number of processing nodes based on actual execution characteristics. The system transitions from static pre-defined resource allocation to dynamic adjustment during task execution, allowing optimal resource utilization that adapts to varying workloads and minimizes overhead costs while maintaining processing speed.
Solution Approach 2:
The patent employs feedback mechanisms where performance measurements from task execution are fed back to the resource allocation system. This feedback loop enables the system to learn from actual execution data and adjust resource allocation strategies accordingly, resolving the contradiction between processing speed and overhead costs by continuously optimizing based on real performance information.
2Productivity
If more processing resources are allocated to parallel processing, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically monitors its own performance, evaluates task execution characteristics, and adjusts resource allocation without external intervention. This autonomous operation simplifies system management by eliminating the need for complex manual configuration and monitoring processes, thereby reducing operational complexity while maintaining high processing efficiency.
Solution Approach 2:
The patent dynamically changes key parameters such as the number of active processing nodes based on actual task requirements and performance metrics. This parameter adjustment approach allows the system to optimize processing efficiency for each specific task while avoiding the complexity of maintaining fixed, overly complex resource allocation configurations.
3Ease of manufacture
If a fixed number of nodes is assigned for parallel processing, then resource allocation is simple, but resource utilization is sub-optimal
Solution Approach 1:
The patent transforms static fixed node assignment into dynamic resource allocation that adapts during task execution. The system continuously adjusts the number of active nodes based on real-time performance metrics, thereby improving resource utilization while maintaining ease of operation through automated decision-making rather than manual configuration.
Data Source
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AI summary
A first performance measurement of an executing task may be determined, while the task is executed by a first number of nodes operating in parallel. A second performance measurement of the executing task may be determined, while the task is being executed by a second number of nodes operating in parallel. An overhead factor characterizing a change of a parallelism overhead of executing the task with nodes executing in parallel may then be calculated, relative to a change in a number of the nodes, based on the first performance measurement and the second performance measurement. Then, an optimal number of nodes to operate in parallel to continue executing the task may be determined, based on the overhead factor.