Cache Directory Region Granularity for False Sharing Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing cache directories in multiprocessor systems is challenging due to varying processing demands and increasing cache and main memory sizes, leading to issues like false sharing and congestion, which degrade performance.
Innovation Solution
Dynamically adjust the region size tracked by each entry in the cache directory based on workload sparsity metrics, increasing the size during sparse workloads to reduce false sharing and decreasing it during dense workloads to maintain accuracy and capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the region size tracked per entry in the cache directory is increased, then false sharing is reduced, but the cache directory capacity decreases
Solution Approach 1:
The patent implements dynamic adjustment of the region size tracked per cache directory entry based on workload sparsity metrics. When workload sparsity increases (indicating more false sharing), the system increases the region size. When workload sparsity decreases, the system decreases the region size to maximize cache directory capacity. This dynamic adaptation resolves the contradiction by making the region size flexible rather than fixed.
Solution Approach 2:
The system changes the parameter of region size based on workload conditions. By monitoring workload sparsity metrics and adjusting the region size parameter accordingly, the system optimizes the balance between reducing false sharing and maintaining cache directory capacity.
2Quantity of substance
If the region size tracked per entry in the cache directory is decreased, then cache directory capacity increases, but false sharing increases
Solution Approach 1:
The system dynamically adjusts region size based on real-time workload sparsity metrics. When the workload becomes denser (lower sparsity), the system decreases the region size to increase cache directory capacity while accepting that false sharing will be more prevalent under these workload conditions.
Solution Approach 2:
The region size parameter is adjusted based on workload characteristics. By decreasing the region size parameter when workload sparsity is low, the system maximizes cache directory capacity to handle the current workload pattern effectively.
3Device complexity
If a fixed region size is used in the cache directory, then implementation is simpler, but performance degrades under varying workload conditions
Solution Approach 1:
The patent transitions from a static fixed region size to a dynamic adjustable region size based on workload sparsity. This adds complexity to the implementation but significantly improves system performance under varying workload conditions by adapting the region size to match the actual access patterns.
Solution Approach 2:
The system implements feedback through workload sparsity metrics to guide region size adjustments. By continuously monitoring access patterns and using this feedback to adjust the region size, the system optimizes performance while managing the complexity through a controlled adaptation mechanism.
Data Source
AI summary
A method includes, in a cache directory, storing a set of entries corresponding to one or more memory regions having a first region size when the cache directory is in a first configuration, and based on a workload sparsity metric, reconfiguring the cache directory to a second configuration. In the second configuration, each entry in the set of entries corresponds to a memory region having a second region size.