Cloud Resource Manager Accelerator Task Assignment
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Solution Overview
Problem
The incorporation of hardware accelerators in compute devices often results in inefficient resource allocation due to varying usage levels and frequent reconfiguration needs, leading to idle times and reduced utilization.
Innovation Solution
A cloud resource manager system that monitors and manages accelerator usage across multiple node compute devices, optimizing task assignment by considering factors like available accelerator images, usage frequency, and hardware requirements to efficiently allocate tasks and minimize reconfiguration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware accelerators are incorporated into compute devices to perform computing tasks more quickly, then processing speed is improved, but resource utilization efficiency deteriorates due to idle times and varying usage levels
Solution Approach 1:
The patent implements a pool of hardware accelerators that can be dynamically allocated to multiple compute devices based on demand. The accelerators serve multiple functions and multiple clients, transitioning between different tasks and users efficiently. This multi-functional approach ensures that accelerators remain productive across different workloads rather than being dedicated to single uses, thereby improving overall resource utilization while maintaining high processing speeds.
Solution Approach 2:
The system employs dynamic task assignment and accelerator allocation mechanisms that adapt to changing workload demands in real-time. The cloud resource manager continuously monitors usage patterns and reassigns accelerators to different compute devices based on current needs, creating a flexible and adaptive resource management system that optimizes both speed and utilization efficiency dynamically.
2Adaptability or versatility
If hardware accelerators are reconfigured frequently to adapt to different tasks, then adaptability is improved, but effective utilization deteriorates due to reconfiguration time
Solution Approach 1:
The system pre-loads and maintains multiple accelerator images in memory, preparing different task configurations in advance. When a task request arrives, the cloud resource manager can quickly switch to a pre-prepared accelerator image that matches the required task type, minimizing reconfiguration time. This preliminary preparation of accelerator configurations allows the system to maintain high adaptability while reducing the productivity loss associated with frequent reconfigurations.
Solution Approach 2:
The patent utilizes software-based accelerator images that can be loaded and switched without physical hardware changes. By changing the operational parameters and configuration through software rather than physical reconfiguration, the system achieves high task adaptability with minimal disruption to effective utilization. The accelerator hardware remains operational while its functional parameters are changed through image switching.
Data Source
AI summary
Technologies for managing accelerator resources include a cloud resource manager to receive accelerator usage information from each of a plurality of node compute devices and task parameters of a task to be performed. The cloud resource manager accesses a task distribution policy. The cloud resource manager determines a destination node compute device of the plurality of node compute devices based on the task parameters and the task distribution policy. The cloud resource manager assigns the task to the destination node compute device. Other embodiments are described and claimed.