Lock-Free Concurrent Collection Using Local Lists
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Solution Overview
Problem
Existing concurrent collection technologies in multi-processor systems face scalability issues due to shared resources and lock contention, limiting performance and reliability in parallel execution environments.
Innovation Solution
The implementation of local lists, either processor or thread local, to create a scalable, lock-free concurrent collection system that reduces contention by allowing each processor or thread to manage its own local list, using an interface for adding and removing elements while ensuring lock freedom and efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If shared resources and locks are used in concurrent collections, then thread safety is ensured, but scalability and performance deteriorate due to lock contention
Solution Approach 1:
The concurrent collection is segmented into multiple local lists, each associated with a different processor or thread. This segmentation eliminates lock contention by allowing each processor to independently access and modify its own local list without acquiring locks, thereby maintaining thread safety while improving scalability and performance in multi-processor systems.
2Stability of the object's composition
If locks are used to ensure thread safety, then data consistency is maintained, but performance and reliability worsen due to blocking and context switches
Solution Approach 1:
By dividing the collection into multiple local lists and allowing each processor to work on its own list without locks, the system maintains data consistency within each local list while eliminating the blocking and context switches associated with lock-based synchronization, thereby improving overall performance.
Solution Approach 2:
The patent introduces an intermediary mechanism where processors can steal elements from other processors' local lists when their own list is empty. This work-stealing approach maintains data consistency while avoiding locks, reducing blocking and context switches, and improving performance.
3Device complexity
If a single shared collection is used, then simplicity is maintained, but scalability deteriorates due to contention between processors
Solution Approach 1:
The collection is divided into multiple local lists that can be independently accessed by different processors. This segmentation improves scalability by reducing contention, and the interface layer maintains simplicity by providing unified add and remove operations that automatically route to appropriate local lists.
Solution Approach 2:
The collection interface provides universal add and remove operations that work across all local lists, maintaining simplicity for users while enabling scalable performance through the underlying segmented structure. The interface abstracts the complexity of multiple local lists while providing consistent functionality.
Data Source
AI summary
A method includes providing a collection that includes a plurality of local lists. Each local list is associated with a different processor or thread in a multi-processor system. An interface to the collection is provided for adding elements to and removing elements from the collection. An add operation is performed with a first processor in the multi-processor system using the interface to add a first element to the collection. The interface is configured to cause the first element to be added to the local list associated with the first processor.