NUMA Garbage Collection via Local Pool Segmentation
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Solution Overview
Problem
Non-uniform memory access (NUMA) computing devices face inefficiencies in memory management due to varying costs of memory access, which existing garbage collection methods fail to optimize effectively, leading to suboptimal system performance and efficiency.
Innovation Solution
The implementation of garbage collector threads that maximize local memory accesses by dividing the heap space into pools and regions, with threads processing objects within local queues and copying Young objects to pools of the same locality group and Old objects to pools associated with the garbage collector thread, thereby minimizing remote memory accesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing garbage collection methods are used in NUMA computing devices, then memory management is performed, but memory access efficiency deteriorates due to unoptimized remote memory accesses
Solution Approach 1:
The heap space is divided into multiple pools, with each pool associated with a specific lgroup. This segmentation allows garbage collector threads to organize and process objects based on their local memory regions, reducing cross-lgroup memory accesses and improving memory management efficiency in NUMA systems.
Solution Approach 2:
The patent implements local queues within each pool that are optimized for local memory access. Garbage collector threads process objects in these local queues first, ensuring that frequently accessed objects remain in local memory regions. This local optimization reduces remote memory accesses and improves overall memory access efficiency.
2Reliability
If garbage collector threads access remote memory regions, then comprehensive garbage collection is achieved, but system performance deteriorates due to increased access cost
Solution Approach 1:
The system segments the heap into multiple pools associated with different lgroups, allowing garbage collector threads to process objects locally within their assigned lgroup first. This segmentation enables comprehensive garbage collection while minimizing remote memory accesses, thus maintaining both reliability and performance.
Solution Approach 2:
The patent implements preliminary organization of objects into local queues within each pool before garbage collection processing. This preliminary action ensures that objects are pre-positioned in local memory regions, allowing garbage collector threads to perform comprehensive collection while minimizing the need for subsequent remote memory accesses.
Data Source
AI summary
Methods and systems for garbage collection are described. In some embodiments, Garbage collector threads may maximize local accesses and minimize remote access by copying Young objects and Old objects differently. When copying a Young object, a garbage collector thread may determine the lgroup of the pool that contains the object and copy the object to a pool of the same lgroup. The garbage collector thread may spread Old objects among lgroups by copying Old objects to pools of the same lgroup as the respective garbage collector thread. Additional methods and systems are disclosed.