Hot Page Detector Histogram Memory Migration
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and migrating hot pages in memory to reduce access times, as they often rely on raw data analysis which can be cumbersome and inefficient.
Innovation Solution
The implementation of a Hot Page Detector (HPD) that utilizes counters and histograms to identify hot pages by configuring a Hot Page Count Threshold (HPCT), allowing for efficient migration of frequently accessed pages to local memory with lower access times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hot page detection is performed using traditional methods, then page access activity can be tracked, but the overhead of migrating pages is high and efficiency is reduced
Solution Approach 1:
The patent extracts only the essential information needed for hot page detection by implementing a histogram that tracks page access counts at aggregated intervals. Instead of analyzing every single access event, the system extracts representative samples at configured intervals (e.g., every 1000 accesses), significantly reducing the data processing overhead while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary action by pre-configuring histogram parameters including access count intervals, number of buckets, and migration thresholds before actual page migration begins. This preliminary configuration establishes the detection framework in advance, allowing the system to efficiently process page accesses without making real-time decisions about histogram structure, thereby reducing migration overhead.
2Speed
If all frequently accessed pages are migrated to local memory, then data access times are reduced, but the complexity and overhead of page management increases
Solution Approach 1:
The patent applies parameter changes by allowing dynamic adjustment of the migration threshold parameter in the histogram. Instead of using a fixed threshold, the system can modify the threshold value based on workload characteristics, access patterns, and performance requirements. This parameter flexibility enables optimization of data access time while managing page management complexity through configurable rather than hard-coded rules.
Solution Approach 2:
The system implements dynamics by making the histogram configuration and migration threshold adjustable and adaptable rather than static. The histogram can be reconfigured with different bucket numbers, access count intervals, and threshold values based on changing system conditions. This dynamic approach allows the page management system to adapt to varying workload patterns without increasing inherent complexity.
3Measurement precision
If detailed analysis of all page accesses is performed, then accurate hot page identification is achieved, but the processing overhead and time consumption increase
Solution Approach 1:
The patent applies partial action by implementing a histogram that tracks only a sampled portion of page accesses rather than every single access event. The system configures the histogram to record access counts at specific intervals (e.g., every N accesses) and uses a limited number of buckets to represent the distribution. This partial sampling approach maintains sufficient accuracy for identifying hot pages while significantly reducing processing overhead compared to analyzing every access event in detail.
Data Source
AI summary
Examples described herein relate to hot page detection. Some examples include circuitry to provide a number of pages with access counts within a bucket of a histogram, wherein the bucket of the histogram is associated with a configured access count range; based on a distribution of access counts in the histogram being a first level, reduce the configured access count ranges of the different buckets of the histogram; determine a second level indicative of page access counts; and migrate data of pages from a far memory to a near memory based on the second level.