GPU Cache Line De-prioritization via DSID Tagging
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Solution Overview
Problem
Current GPU memory cache management is inefficient due to the lack of optimal prioritization and flushing mechanisms, leading to increased power consumption and latency, especially in battery-powered devices, as the memory cache is unaware of when the GPU is done utilizing resources, causing unnecessary data movement to lower level memory.
Innovation Solution
Implementing a multi-level priority scheme with constant and adjustable hardware data-set identifiers (DSIDs) for memory cache requests, allowing for tagged and untagged requests, and using de-prioritize commands to flush and invalidate cache lines when higher priority content is received, thereby optimizing cache management and reducing power consumption and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the memory cache retains all data indefinitely without prioritization, then data availability is improved, but power consumption increases due to unnecessary data retention and movement to lower level memory
Solution Approach 1:
The patent segments the memory cache into multiple priority levels (first priority and second priority cache lines) based on DSID assignments. This segmentation allows the cache to selectively retain or flush data based on priority, avoiding the energy waste of retaining all data indefinitely while ensuring high-priority data remains available.
Solution Approach 2:
The patent implements dynamic priority assignment where DSIDs can be reassigned between different data sets over time. The de-prioritize command allows cache lines to be dynamically flushed from first priority to second priority, enabling the system to adapt to changing data availability needs while reducing power consumption by only retaining essential data.
2Use of energy by moving object
If the memory cache flushes data frequently to lower level memory, then power consumption is reduced, but processing latency increases due to data movement overhead
Solution Approach 1:
The patent uses dynamic priority management where data is flushed from first priority to second priority cache lines only when de-prioritized, rather than frequent unconditional flushing. This dynamic approach minimizes unnecessary data movement while still enabling power savings, thereby reducing latency compared to frequent flushing schemes.
Solution Approach 2:
The patent applies different retention policies to different cache lines based on their priority level. First priority cache lines retain data longer to avoid latency, while second priority cache lines are flushed more aggressively to save power. This localized quality differentiation resolves the contradiction between power consumption and latency.
3Device complexity
If the memory cache uses a simple LRU (Least Recently Used) policy, then implementation complexity is reduced, but performance optimization is limited due to inability to handle priority-based data retention
Solution Approach 1:
The patent segments the cache into priority levels and associates DSIDs with cache lines, creating a structured priority-based system. While more complex than simple LRU, this segmentation enables the GPU to optimize performance by retaining high-priority data and flushing low-priority data, directly addressing the performance limitation of basic LRU policies.
Solution Approach 2:
The de-prioritize command enables the cache system to automatically manage its own priority levels and data retention policies based on GPU needs. This self-service mechanism allows the cache to optimize performance without requiring complex external management, balancing implementation complexity with performance benefits.
4Speed
If the cache retains all data at high priority, then data access speed is improved, but the cache cannot accommodate higher priority content when needed
Solution Approach 1:
The patent implements dynamic priority reassignment where DSIDs can be reassigned from first priority to second priority cache lines based on current GPU needs. This allows the cache to quickly accommodate higher priority content by flushing lower priority data, while maintaining fast access speeds for currently prioritized data through selective retention.
Data Source
AI summary
Systems, methods, and computer readable media to manage memory cache for graphics processing are described. A processor creates a resource group for a plurality of graphics application program interface (API) resources. The processor subsequently encodes a set command that references the resource group within a command buffer and assigns a data set identifier (DSID) to the resource group. The processor also encodes a write command within the command buffer that causes the graphics processor to write data within a cache line and mark the written cache line with the DSID, a read command that causes the graphics processor to read data written into the resource group, and a de-prioritize command that causes the graphics processor to notify the memory cache to later flush content from the cache line associated with the DSID and to later invalidate the cache line when higher priority content is received.