GPU Workload Reordering for On-Chip Resource Residency
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Solution Overview
Problem
Current graphics processing techniques result in resources being evicted off-chip, leading to increased power consumption and decreased performance due to inefficient management of graphics processor workloads.
Innovation Solution
A method to reorder graphics processor workloads such that producers and consumers of resources are closely sequenced, preserving functional ordering and minimizing off-chip transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If graphics processor workloads are executed in the original submission order, then the execution pipeline remains simple and straightforward, but resources are evicted off-chip leading to increased power consumption and decreased performance
Solution Approach 1:
The system performs preliminary analysis of the workload chain to identify producer-consumer relationships before execution. By determining which workloads produce resources that subsequent workloads consume, the system can pre-calculate the optimal reordering to minimize off-chip evictions, thereby reducing power consumption while maintaining high processing performance
Solution Approach 2:
The workload execution order is made dynamic rather than static. The system continuously monitors resource production and consumption patterns and adjusts the execution order accordingly, allowing flexible reconfiguration of workload chains to optimize resource retention on-chip and minimize evictions, thus balancing performance and energy efficiency
2Productivity
If graphics processor workloads are reordered to minimize off-chip transactions, then power consumption decreases and performance increases, but the execution order management becomes more complex
Solution Approach 1:
The system implements feedback mechanisms to monitor resource eviction patterns and adjust workload ordering accordingly. By analyzing which resources are frequently evicted and which workloads benefit from closer sequencing, the system continuously optimizes the execution order, managing complexity through data-driven adjustments rather than arbitrary reordering
Solution Approach 2:
An intermediary workload chain manager is introduced between the workload submission interface and the GPU execution pipeline. This mediator analyzes producer-consumer relationships, calculates optimal reordering, and presents a simplified execution schedule to the GPU, abstracting away the complexity of optimization while maintaining high performance
3Reliability
If producers and consumers of resources are closely sequenced, then resource residency on-chip is maximized, but the original functional ordering of workloads may be disrupted
Solution Approach 1:
The workload chain is segmented into distinct producer and consumer groups based on resource dependencies. By separating workloads into these functional segments, the system can independently optimize the sequencing within each segment while maintaining the overall functional integrity, allowing resource-resident ordering without compromising essential functional relationships
Solution Approach 2:
The system changes the temporal parameter of workload execution by introducing delays or reordering operations based on resource usage patterns. By adjusting execution timing parameters rather than fundamentally altering the functional sequence, the system maintains logical ordering while optimizing for resource retention on-chip
Data Source
AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for reordering chains of producing and consuming workloads to exploit GPU memory residency. A processor obtains an indication of a set of graphics processor workloads that are to be executed by a graphics processor in an execution order. The processor determines that a first subset of graphics processor workloads in the set of graphics processor workloads produces a set of resources in graphics memory and that a second subset of graphics processor workloads in the set of graphics processor workloads consumes the set of resources in the graphics memory. The processor alters, based on the determination, the execution order such that a first index of the first subset occurs within a threshold index separation from a second index of the second subset. The processor outputs an indication of the altered execution order.