Physical Memory Address Clustering for Active-Region Prefetching
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Solution Overview
Problem
Existing memory access technologies face challenges in efficiently managing increasing data demands and latency differences between volatile and non-volatile storage media, particularly in predicting data prefetching due to the overhead of translating offline-generated heat maps to physical addresses.
Innovation Solution
A two-level clustering model is employed to group physical memory addresses adaptively, using clusters and active regions, with a prefetch engine monitoring activity levels and making prefetching decisions based on access profiles to minimize cache misses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in non-volatile memory, then storage capacity increases, but data access speed decreases
Solution Approach 1:
The system performs preliminary actions by proactively prefetching data from non-volatile memory to volatile memory before actual access requests occur. The prefetch engine identifies active regions and predicts future access patterns, loading data in advance to eliminate latency when data is needed.
Solution Approach 2:
The patent introduces volatile memory as an intermediary between non-volatile memory and the processor. Data is transferred from non-volatile memory to volatile memory in advance, creating a buffer that provides fast access to frequently used data while maintaining the large storage capacity of non-volatile memory.
2Loss of time
If prefetching is performed, then data access latency is reduced, but system complexity increases
Solution Approach 1:
The memory space is segmented into active regions and inactive regions based on access patterns. The system divides memory management into distinct components: a prefetch engine for identifying active regions, a translation engine for address mapping, and a data transfer mechanism. This segmentation simplifies the prefetching logic by focusing only on active regions rather than managing entire memory spaces.
Solution Approach 2:
The system uses the access patterns generated by applications themselves to drive the prefetching process. The prefetch engine monitors actual access behavior and uses this information to automatically identify active regions and trigger prefetching operations, eliminating the need for external control or complex manual configuration.
3Measurement precision
If heat map translation is performed, then prefetching accuracy improves, but processing overhead increases
Solution Approach 1:
Instead of translating and processing entire heat maps, the system performs partial action by focusing only on active regions that are actually being accessed. The prefetch engine identifies and processes only the relevant portions of memory space, avoiding the overhead of translating complete heat maps while maintaining prefetching accuracy for the important data.
Data Source
AI summary
Systems and methods for grouping memory addresses are disclosed. A storage device may include a first storage medium, a second storage medium, and a processor configured to: identify a first memory address; identify a first region of the first storage medium based on the first memory address; track activity of the first region; identify a second memory address based on tracking activity of the first region; and retrieve data associated with the second memory address from the first storage medium and store the data in the second storage medium.