GPU Thread Dispatch for Power Optimization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current graphics processing systems face inefficiencies in parallel data processing, particularly in maximizing parallelism and optimizing thread scheduling across compute blocks for power management, leading to suboptimal performance and power utilization.

Innovation Solution

Implementing a graphics processing unit (GPU) architecture with a parallel processor design that includes a scheduler and thread dispatcher to dynamically distribute threads across compute clusters, utilizing thread scheduling logic and memory address pattern analysis to optimize thread dispatch and power management, thereby maximizing parallel processing efficiency and reducing power consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If threads are distributed across multiple compute blocks to maximize parallelism, then processing throughput is improved, but power consumption increases

Engineering Contradiction:

Improveprocessing throughputVSAvoidpower consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts thread distribution across compute blocks based on workload characteristics and power constraints. The scheduler monitors parallelism requirements and power consumption in real-time, adapting the degree of thread distribution to achieve optimal balance between throughput and power usage.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The invention changes the parameter of thread distribution granularity based on workload analysis. By analyzing parallelism profiles and workload characteristics, the system adjusts whether to distribute threads broadly across many compute blocks or concentrate them in fewer blocks, thereby controlling power consumption while maintaining processing efficiency.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If thread scheduling is optimized for maximum parallelism, then processing efficiency is improved, but hardware resource utilization becomes suboptimal

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidhardware resource utilization

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of workload parallelism profiles before thread dispatch. By analyzing the parallelism characteristics of incoming workloads in advance, the scheduler can pre-determine the optimal thread distribution strategy, avoiding both over-provisioning and under-provisioning of hardware resources.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The invention implements feedback mechanisms where the scheduler monitors actual thread execution patterns and hardware resource utilization. Based on this feedback, the system adjusts future thread distribution decisions to improve both processing efficiency and hardware utilization, correcting suboptimal resource allocation over time.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240362741A1Thread scheduling over compute blocks for power optimization

Publication Date: 2024.10.31 INTEL CORP

AI summary

One embodiment provides for a general-purpose graphics processing unit comprising a processing array including multiple compute blocks, each compute block including multiple processing clusters and a thread dispatch unit to dispatch threads of a workload to the multiple compute blocks based on a parallelism metric, wherein the thread dispatch unit, based on the parallelism metric, is to perform one of a first operation and a second operation, the first operation to distribute threads across the multiple compute blocks and the second operation is to concentrate threads within one of the multiple compute blocks.