Memory Controller Heatmap Data Placement Optimization
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Solution Overview
Problem
Current memory management systems in computer memory hierarchies lack an efficient method to optimize data placement across different memory layers with varying performance characteristics, leading to suboptimal data access and storage decisions.
Innovation Solution
Implementing a memory controller that generates a heatmap of data access frequencies, allowing for dynamic configuration and optimization of data placement by mapping frequently accessed data to faster memory and less frequently accessed data to slower, higher-capacity memory, using access frequency counters and algorithms to manage counter values and prevent overflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is placed in faster memory layers, then data access time is improved, but memory capacity and cost increase
Solution Approach 1:
The patent applies local quality by creating heatmaps that identify specific local regions of memory with high access frequencies. Instead of uniformly treating all memory, the system selectively identifies hot spots and remaps only those specific local regions to faster memory layers, while leaving cold regions in slower, higher-capacity memory. This resolves the contradiction by optimizing access speed for only the necessary portions of data rather than the entire memory space.
Solution Approach 2:
The patent implements dynamics through dynamic remapping that continuously adapts to changing access patterns. The memory controller monitors access frequencies over time and dynamically adjusts the mapping between virtual addresses and physical memory locations based on evolving heatmaps. This allows the system to transition data between memory layers as access patterns change, optimizing the balance between access speed and memory capacity utilization.
2Quantity of substance
If data is placed in higher-capacity memory, then storage efficiency is improved, but data access time increases
Solution Approach 1:
The patent applies preliminary action by proactively identifying data that is likely to be accessed soon based on historical access patterns captured in heatmaps. The system pre-remaps frequently accessed data to faster memory layers before actual access occurs, ensuring that when the data is needed, it is already in the optimal location for fast access. This prevents the performance penalty of data being in slow memory when first accessed.
Solution Approach 2:
The patent implements feedback through a closed-loop system where the memory controller continuously monitors actual access patterns, updates heatmaps based on observed behavior, and adjusts remapping decisions accordingly. This feedback mechanism ensures that the system learns from actual usage and optimizes the placement of data in memory layers, dynamically balancing capacity utilization with access speed based on real-world performance data.
3Productivity
If memory remapping is performed frequently, then data placement optimization is improved, but system overhead increases
Solution Approach 1:
The patent applies periodic action by updating heatmaps and performing remapping operations at scheduled intervals rather than continuously. The system collects access pattern data over defined time periods, processes this information to identify optimization opportunities, and then performs remapping operations periodically. This approach balances the need for optimization with the cost of overhead by concentrating remapping activities into periodic batches rather than continuous operations.
Solution Approach 2:
The patent implements parameter changes by adjusting the granularity and thresholds of heatmap collection and remapping triggers. The system can modify parameters such as the time window for collecting access patterns, the minimum access frequency threshold for triggering remapping, and the granularity of memory regions considered. These parameter adjustments allow the system to optimize the balance between placement optimization effectiveness and the overhead cost based on specific workload characteristics.
Data Source
AI summary
Embodiments of the present disclosure provide an enhanced system and methods for optimizing data placement in a memory hierarchy. A disclosed non-limiting computer-implemented method configures a counter block comprising access frequency counters mapped into an application memory space, and configures a counter map, where each entry in the counter map associates an application-defined memory region with the access frequency counters of the counter block. A memory controller identifies a memory access in a given application-defined memory region and compares an access address with a mask in the counter map to track the memory access. The memory controller generates a heatmap representing a frequency count of accesses to quantized memory using the access frequency counters. Generating the heatmap is performed by memory controller hardware.