Phase-Dependent Memory Residency Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In modern computing systems, the overhead of managing virtual address mappings becomes a significant performance limiter as memory usage increases due to multiple applications running concurrently, necessitating improvements in memory optimization.
Innovation Solution
The implementation of phase-dependent data residency, where application programs are profiled to identify and proactively remove memory regions no longer needed during phase transitions and preload regions anticipated for future use, with a hypervisor monitoring and coordinating these actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If virtual memory mapping is used to allow applications to access more memory than physical memory exists, then memory capacity is improved, but system performance deteriorates due to overhead of managing virtual address mappings
Solution Approach 1:
The patent profiles application memory usage patterns in advance (a priori or in real time) to identify which memory regions will be needed in future phases. This preliminary analysis enables the system to proactively preload memory regions before they are actually accessed, reducing the overhead of virtual memory management during runtime by having data ready in physical memory when needed.
Solution Approach 2:
The patent implements dynamic memory management by continuously monitoring application phase transitions and adapting memory residency accordingly. Memory regions are dynamically removed from physical memory when the application transitions to a new phase that doesn't require them, and dynamically preloaded when future phases are anticipated. This dynamic approach optimizes the balance between memory capacity utilization and system performance.
2Speed
If memory regions are kept resident in physical memory for all application phases, then access speed is improved, but memory usage increases unnecessarily
Solution Approach 1:
Instead of keeping all memory regions resident throughout the entire application lifecycle, the system performs preliminary profiling to identify which regions will be needed in future phases. Only these identified regions are preloaded and kept resident, while other regions are removed from physical memory after their current phase completes, thus maintaining fast access for needed data while reducing overall memory usage.
Solution Approach 2:
The patent changes the memory residency parameter dynamically based on application phase. Memory regions transition between resident and non-resident states depending on the current and anticipated phases of the application. This parameter change allows the system to optimize between access speed and memory usage by having regions resident only when they are actually needed.
3Quantity of substance
If memory regions are removed from physical memory to free up space, then memory availability is improved, but access time increases due to virtual memory swapping
Solution Approach 1:
The system performs preliminary profiling to predict which memory regions will be needed in future phases. By proactively preloading these regions into physical memory before they are actually accessed, the system avoids the time penalty of virtual memory swapping. Memory regions are removed from physical memory only after their current phase completes and before they are needed again, thus freeing up space without causing access delays.
Data Source
AI summary
Embodiments of the present invention provide memory optimization by phase-dependent data residency. Application programs are profiled a priori or in real time for temporal memory usage. Memory regions such as initialization data are proactively removed from memory when the application transitions to a new phase. A hypervisor monitors application activity and coordinates the removal of memory regions that are no longer needed by the application. Additionally, memory regions that are anticipated to be needed in the future are proactively preloaded.