NUMA Garbage Collection via Local Memory Pool Segmentation
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Solution Overview
Problem
In non-uniform memory access (NUMA) computing devices, existing garbage collection methods fail to maximize local memory accesses and minimize remote memory accesses efficiently, leading to suboptimal system performance and efficiency.
Innovation Solution
The method involves assigning processing units and memories to locality groups (Igroups) based on access latency, dividing the heap space into pools, and utilizing garbage collector threads with separate queues for local and remote Igroups to process objects, thereby maximizing local memory accesses and minimizing remote ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing garbage collection methods are used in NUMA computing devices, then garbage collection can be performed, but local memory accesses are not maximized and remote memory accesses are not minimized efficiently
Solution Approach 1:
The heap space is divided into multiple pools, with each pool assigned to a specific Igroup. This segmentation allows garbage collector threads to process objects in pools based on their Igroup affiliation, maximizing local memory accesses and minimizing remote accesses by keeping objects within their originating Igroup when possible.
Solution Approach 2:
Different pools are assigned to different Igroups based on memory access latency characteristics. Garbage collector threads are assigned to specific Igroups and preferentially process pools within their assigned Igroup, creating a localized processing quality that optimizes memory access patterns for each thread's operational context.
2Quantity of substance
If memory is accessed remotely in NUMA computing devices, then more memory pools can be utilized, but system performance and efficiency deteriorate due to increased access latency
Solution Approach 1:
Pools are pre-assigned to specific Igroups based on memory access latency characteristics before garbage collection begins. This preliminary assignment ensures that when garbage collection occurs, threads can immediately access pools within their assigned Igroup without incurring remote access latency, while still having the option to access pools in other Igroups when necessary.
3Loss of time
If Young objects are kept local in the same Igroup, then memory access latency is reduced, but memory usage balance across Igroups may deteriorate
Solution Approach 1:
The system dynamically manages object placement by Igroup based on the object's age and lifecycle stage. Young objects are kept local to their originating Igroup to minimize access latency, while Old objects are distributed across multiple Igroups to balance memory usage. This dynamic approach allows the system to optimize for different priorities at different stages of object lifecycle.
Solution Approach 2:
The garbage collector periodically redistributes Old objects across different Igroups during collection cycles. This periodic action prevents any single Igroup from becoming overloaded with Old objects, thereby maintaining memory usage balance across the system while allowing Young objects to remain local for optimal access performance.
Data Source
AI summary
System and Methods for non-uniform memory (NUMA) garbage collection are provided. Multiple memories and processors are categorized into local groups. A heap space is divided into multiple pools and stored in each of the memories. Garbage collection threads are assigned to each of the local groups. Garbage collection is performed using the garbage collection threads for objects contained in the pools using the garbage collector threads, memory, and processor assigned to each local group, minimizing remote memory accesses.