Dynamic UMA-NUMA Memory Partitioning for Workload-Adaptive Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional SoC designs face challenges in balancing memory access latency, power consumption, and memory management complexity due to the fixed choice between UMA and NUMA architectures, which are suboptimal for varying workloads.
Innovation Solution
A dynamically configurable memory architecture that partitions memory into UMA and NUMA regions with a software-adjustable boundary point, allowing dynamic adjustment based on workload parameters to optimize performance and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If UMA architecture is used, then memory management complexity is reduced, but memory access latency increases and power consumption increases
Solution Approach 1:
The memory system is segmented into multiple memory regions, with each region managed by a separate memory controller that can be independently configured as UMA or NUMA. This allows different segments to use different architectures optimized for their specific workload requirements, resolving the contradiction by enabling low-latency NUMA access where needed while maintaining simple UMA management where applicable.
Solution Approach 2:
The memory architecture employs dynamic configuration where memory regions can be switched between UMA and NUMA modes based on runtime workload characteristics. The system monitors access patterns and dynamically adjusts the memory management mode, allowing it to adapt to changing requirements and optimize both latency and management complexity under different operating conditions.
2Device complexity
If UMA architecture is used, then memory management complexity is reduced, but power consumption increases
Solution Approach 1:
The memory system is divided into multiple regions with independent memory controllers that can be configured as UMA or NUMA. This segmentation allows power-efficient NUMA architecture to be applied to specific memory regions with high access frequency, while other regions use simpler UMA management, thereby reducing overall power consumption without sacrificing management simplicity where NUMA is not required.
Solution Approach 2:
The system dynamically changes the memory management parameters (UMA/NUMA configuration) based on workload characteristics and power requirements. By adjusting the configuration mode according to real-time conditions, the system optimizes the balance between management complexity and power consumption, selecting the appropriate architecture for each memory region based on its access patterns and importance.
3Loss of time
If NUMA architecture is used, then memory access latency is reduced, but memory management complexity increases
Solution Approach 1:
The memory system is segmented into multiple regions, each with its own memory controller that can be independently configured. This allows NUMA architecture to be applied only to specific memory regions where low latency is critical, while other regions use simpler UMA management, thereby achieving low-latency access where needed without incurring the full overhead of NUMA management across the entire system.
Solution Approach 2:
Different memory regions are assigned different management architectures based on their specific requirements. Regions with high access frequency and performance-critical workloads use NUMA for low latency, while regions with less demanding requirements use UMA for simplicity. This local optimization resolves the contradiction by applying complexity only where it provides necessary performance benefits.
4Device complexity
If fixed memory architecture is used, then design simplicity is maintained, but adaptability to varying workloads decreases
Solution Approach 1:
The memory architecture implements dynamic configurability where each memory region can be switched between UMA and NUMA modes based on runtime workload characteristics. The system monitors access patterns, latency requirements, and power consumption, then dynamically adjusts the configuration to match current demands, thereby achieving high adaptability while maintaining relatively simple design through modular memory controllers.
Solution Approach 2:
The memory system is designed with universal memory controllers that can operate in multiple modes (UMA and NUMA). Each controller is capable of performing both simple UMA management and complex NUMA management, allowing the same hardware infrastructure to adapt to different workload requirements without requiring separate dedicated controllers for each architecture type.
Data Source
Figure 1~2
Figure 3~4
Figure 5A~5E
AI summary
The description relates to dynamic memory management. One example includes an assembly that entails processing elements and memory. A dynamic UMA/NUMA configuration module is configured to facilitate managing a first region of the memory based upon a Uniform Memory Access (UMA) architecture and a second region of the memory based upon a Non-Uniform Memory Access (NUMA) architecture. The dynamic UMA/NUMA configuration module is configured to dynamically adjust ratios of the memory in the first region and the second region based upon workload changes on the processing elements.