Cache Footprint Management via Dynamic Thread Arbitration
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Solution Overview
Problem
Cache thrashing occurs when the size of the data set exceeds the cache size, leading to repeated eviction and re-fetching of data, which reduces processor performance and increases power consumption, particularly in graphics processors with multiple shader pipelines and central processing units executing multiple threads in parallel.
Innovation Solution
Control circuitry tracks cache performance metrics to limit the number of threads considered for arbitration, thereby controlling the cache footprint by adjusting the number of SIMD groups executed concurrently, reducing cache thrashing and improving processor performance and power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of concurrently executed threads or SIMD groups is increased to improve processor utilization, then execution throughput is improved, but cache thrashing occurs when the data set size exceeds cache capacity
Solution Approach 1:
The system dynamically adjusts the number of concurrently executed threads or SIMD groups based on real-time cache performance metrics. The scheduler monitors cache hit/miss ratios and adjusts thread concurrency levels dynamically, transitioning from static to adaptive control to optimize both throughput and energy efficiency.
Solution Approach 2:
The system implements a feedback mechanism where cache performance metrics (hit/miss ratios, eviction rates) are continuously monitored and fed back to the scheduler. This feedback loop enables the scheduler to adjust thread concurrency levels in response to cache conditions, preventing cache thrashing while maintaining optimal execution throughput.
2Productivity
If more threads are scheduled for arbitration to maximize processor utilization, then execution productivity improves, but cache performance deteriorates due to increased cache footprint
Solution Approach 1:
The scheduler transitions from static thread scheduling to dynamic scheduling that adapts to cache conditions. The system adjusts the number of threads submitted for arbitration based on real-time cache performance, enabling flexible optimization of both productivity and cache reliability.
Solution Approach 2:
Cache performance metrics are continuously monitored and fed back to the scheduler, which adjusts thread arbitration accordingly. This feedback mechanism ensures that thread scheduling decisions are made with knowledge of current cache state, preventing performance deterioration.
3Loss of energy
If the cache footprint is reduced by limiting concurrent thread execution, then cache thrashing is reduced, but processor utilization decreases
Solution Approach 1:
The system employs dynamic control of thread concurrency levels rather than fixed limitations. The scheduler adjusts the number of concurrent threads based on cache performance feedback, allowing the system to maintain high processor utilization when cache conditions are good while reducing utilization when cache thrashing is detected.
Solution Approach 2:
The system changes the operational parameters of thread execution by adjusting concurrency levels based on cache performance. This parameter adjustment allows optimization of energy efficiency without permanently reducing processor utilization capability.
4Productivity
If concurrent execution of multiple SIMD groups is increased to improve graphics processing throughput, then rendering productivity improves, but cache evictions increase leading to performance degradation
Solution Approach 1:
The graphics processor scheduler dynamically adjusts the number of concurrently executed SIMD groups based on cache performance metrics. This dynamic adjustment optimizes rendering throughput while preventing cache thrashing that would increase processing time.
Solution Approach 2:
The system implements feedback from cache performance metrics to the SIMD group scheduler, enabling real-time optimization of rendering throughput while avoiding cache evictions that would degrade processing time.
Data Source
AI summary
Techniques are disclosed relating to cache footprint management. In some embodiments, execution circuitry is configured to perform operations for instructions from multiple threads in parallel. Cache circuitry may store information operated on by threads executed by the execution circuitry. Scheduling circuitry may arbitrate among threads to schedule threads for execution by the execution circuitry. Tracking circuitry may determine one or more performance metrics for the cache circuitry. Control circuitry may, based on the one or more performance metrics meeting a threshold, reduce a limit on a number of threads considered for arbitration by the scheduling circuitry, to control a footprint of information stored by the cache circuitry. Disclosed techniques may advantageously reduce or avoid cache thrashing for certain processor workloads.