GPU Two-Level Binning Mode Selection for Power and Thermal Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In battery-powered devices, there is a tradeoff between GPU performance and battery life/thermals, with existing technologies struggling to efficiently manage these competing demands.
Innovation Solution
A GPU selects a binning mode based on performance characteristics and workload priorities, using techniques like two-level binning to adapt rendering processes, reducing power consumption, and managing temperatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional binning modes are used to maintain GPU performance, then processing speed is improved, but power consumption and heat generation increase
Solution Approach 1:
The patent implements dynamic binning mode selection that adapts to real-time performance characteristics and workload priorities. The system transitions between different binning modes (traditional vs. two-level) based on runtime conditions, allowing the GPU to optimize between performance and power consumption dynamically rather than using a fixed mode
Solution Approach 2:
The system changes the binning parameter configuration based on performance characteristics and workload priority. By adjusting the binning mode parameter (traditional vs. two-level) according to runtime conditions, the system optimizes the balance between processing speed and power consumption for different scenarios
2Productivity
If traditional binning modes are used to maintain GPU performance, then processing speed is improved, but temperature increases
Solution Approach 1:
The system dynamically adjusts binning mode based on real-time performance characteristics and workload priorities, allowing the GPU to transition between traditional and two-level binning modes to manage temperature while maintaining acceptable performance levels under thermal constraints
Solution Approach 2:
The system modifies the binning mode parameter configuration based on runtime performance characteristics, enabling the GPU to switch to more power-efficient two-level binning when thermal management is required while maintaining processing capability
3Use of energy by moving object
If two-level binning is used to reduce power consumption, then energy efficiency is improved, but processing speed may decrease
Solution Approach 1:
The system implements dynamic selection between traditional and two-level binning modes based on workload priority and performance characteristics. High-priority workloads can utilize traditional binning for maximum speed, while lower-priority workloads use two-level binning for power efficiency, optimizing the overall energy-performance balance
Solution Approach 2:
The system applies different binning modes to different workloads based on their priority levels and performance requirements. Instead of using a single binning mode for all tasks, the system tailors the binning approach locally to each workload's specific needs
4Loss of time
If priority-based workload management is implemented, then important tasks are completed faster, but system complexity increases
Solution Approach 1:
The system segments workloads into different priority levels and processes them through separate queues. This segmentation allows high-priority workloads to be handled separately from lower-priority ones, enabling faster completion of critical tasks while maintaining organized system management
Data Source
AI summary
Systems and methods related to priority-based and performance-based selection of a render mode, such as a two-level binning mode, in which to execute workloads with a graphics processing unit (GPU) of a system are provided. A user mode driver (UMD) or kernel mode driver (KMD) executed at a central processing unit (CPU) configures low and medium priority workloads to be executed in a two-level binning mode and selects a binning mode for high priority workloads based on whether performance heuristics indicate that one or more binning conditions or override conditions have been met. High priority workloads are maintained in a high priority queue, while low and medium priority workloads are maintained in a low/medium priority queue, such that execution of low and medium priority workloads at the GPU can be preempted in favor of executing high priority workloads.