GPU Resource Allocation for Concurrent Workloads Under QoS Constraints
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Solution Overview
Problem
Current GPU scheduling solutions fail to dynamically allocate resources based on application quality of service (QoS) requirements, leading to inefficiencies and inability to prioritize processes with real-time needs.
Innovation Solution
A method and system for dynamically modifying GPU resource allocations among concurrent processes at runtime using a preconfigured resource allocation policy, considering factors like QoS, resource utilization, and power consumption, based on a state of GPU resource assignments and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GPU resources are allocated statically at boot time or with predefined percentages, then device complexity is reduced and ease of operation is improved, but adaptability to runtime requirements and productivity are worsened
Solution Approach 1:
The patent implements dynamic resource allocation by transitioning from static boot-time or predefined percentage-based GPU resource allocation to a runtime system that continuously monitors application performance metrics and QoS requirements. The allocation percentages are adjusted dynamically during execution based on actual workload characteristics, enabling the system to adapt to changing runtime requirements while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system employs feedback mechanisms by monitoring application performance metrics, QoS requirements, and resource utilization in real-time. This feedback loop enables the resource allocation system to automatically adjust GPU resource distribution based on actual application behavior and performance needs, resolving the contradiction between adaptability and complexity through data-driven automated decisions.
2Productivity
If GPU scheduling minimizes idle time to improve productivity, then resource utilization increases, but quality of service requirements and measurement precision of application performance are worsened
Solution Approach 1:
The patent changes the parameters of resource allocation from fixed static values to dynamic percentages that are adjusted based on application performance metrics and QoS requirements. By continuously monitoring parameters such as application priority, resource utilization, and performance thresholds, the system optimizes GPU resource distribution to simultaneously improve productivity through reduced idle time and maintain reliability by meeting QoS requirements.
Solution Approach 2:
The system uses feedback from performance monitoring to balance productivity and QoS requirements. By continuously measuring application performance and resource utilization, the system can dynamically adjust allocations to ensure high GPU utilization while preventing QoS violations, resolving the contradiction between maximizing productivity and maintaining service quality.
3Measurement precision
If static resource allocation is used to simplify the system, then ease of manufacture and operation are improved, but the ability to prioritize applications and measure performance precisely is worsened
Solution Approach 1:
The patent implements a self-service resource allocation system that automatically monitors application performance, evaluates QoS requirements, and adjusts GPU resource allocations without manual intervention. This automated self-managing approach enables precise performance measurement and dynamic prioritization while keeping the system relatively simple by eliminating the need for complex manual configuration and intervention mechanisms.
Data Source
AI summary
While the capabilities of GPUs are being consistently enhanced with each new generation thereby enabling them to process data at a faster rate, many applications configured to execute on the GPU do not exploit the full potential of a GPU. To better utilize GPU resources and to more efficiently run applications, applications can be co-scheduled on the GPU such that the GPU concurrently executes processes of the co-scheduled applications. However, current GPU scheduling solutions are limited in that they either do not consider the QoS requirements of an application or do not allow for dynamic allocations during application execution. The present disclosure provides for dynamic allocation of GPU resources for concurrent processes which can optimize GPU resource utilization while minimizing power consumption and adhering to QoS requirements of each application.