NUMA Memory Pool Rebalancing for Asymmetric CPU Workloads
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Solution Overview
Problem
Existing memory allocation models, such as the uniform memory access (UMA) model, result in inefficient memory usage and increased latency due to the need for remote memory access, which is slower than local access, especially in systems with varying workloads and asymmetric CPU core distributions.
Innovation Solution
Implementing a non-uniform memory access (NUMA) model combined with a dynamic asymmetric CPU core model to dynamically redistribute memory slices based on CPU core workload changes, optimizing memory allocation to improve performance and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a uniform memory access (UMA) model is used for memory allocation, then memory allocation is simple and uniform, but memory access latency increases and bandwidth decreases due to remote memory access
Solution Approach 1:
The patent applies local quality by allocating memory locally to each CPU socket (first memory to first socket, second memory to second socket) rather than uniformly across all sockets. This ensures that CPU cores access memory with locally optimal characteristics, reducing latency while maintaining manageable allocation complexity through the NUMA architecture.
Solution Approach 2:
The patent implements dynamics by dynamically redistributing memory slices between first and second memory pools based on real-time CPU core workload changes. When workload shifts occur, the system automatically adjusts memory allocation to match current demands, optimizing access latency without requiring complex static pre-allocation schemes.
2Device complexity
If memory is statically allocated to memory pools, then memory management is simple, but system adaptability to varying workloads decreases
Solution Approach 1:
The system transitions from static to dynamic memory allocation by continuously monitoring CPU core workload and automatically redistributing memory slices between memory pools. This dynamic adjustment mechanism maintains relatively simple management complexity while dramatically improving workload adaptability, as memory resources automatically follow computational demands.
Solution Approach 2:
The patent implements feedback by monitoring CPU core workload changes and using this information to drive memory redistribution decisions. The system continuously gathers workload metrics, processes this feedback, and adjusts memory allocation accordingly, creating a closed-loop control system that adapts to varying workloads while maintaining manageable complexity through automated decision-making.
3Productivity
If CPU cores are asymmetrically distributed with varying workloads, then processing capacity is optimized, but memory access efficiency deteriorates due to remote access requirements
Solution Approach 1:
The patent applies local quality by ensuring that CPU cores accessing memory pools do so through locally-optimized memory paths. First socket cores primarily access first memory, and second socket cores primarily access second memory, minimizing remote access requirements and maintaining high memory access speeds that support the asymmetric CPU distribution and varying workloads.
Solution Approach 2:
The system implements dynamics by dynamically adjusting memory slice allocation in response to changing workload patterns. When workload distribution changes, memory allocation automatically adapts to ensure that memory access speed requirements are met, preventing the deterioration of access efficiency even as processing capacity demands evolve.
Data Source
AI summary
Techniques can include: performing an initial allocation of memory slices for memory pools from a first memory local to a first socket and a second memory local to a second socket, wherein the initial allocation of memory slices from the first and second memories for each memory pool is based, at least in part, on first information denoting corresponding quantities of CPU cores of the first and second sockets that utilize each memory pool; determining second information including corresponding quantities of CPU cores of the first and second sockets that utilize each memory pool; determining changes between corresponding quantities of CPU cores of the first information and the second information for any of the first socket and the second socket for one or more of the memory pools; and performing dynamic redistribution of memory slices of the one or more memory pools based, at least in part, on the changes.