Sector-Based Tracking for Page Cache Dirty Data
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Solution Overview
Problem
Current memory subsystems face challenges in maintaining and tracking redundant copies of dirty data within a cache, leading to potential data loss due to uncorrectable errors or failures, especially in systems requiring high reliability where error checking and correction are limited.
Innovation Solution
Implementing a dynamically sized redundant write buffer within a cache with sector-based tracking, where original and redundant copies of dirty data are written to separate cache lines, allowing for efficient tracking and recovery of data, and omitting clean data during migration to non-volatile memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant copies of dirty data are maintained in cache, then data reliability is improved, but cache space consumption increases
Solution Approach 1:
The cache is segmented into clean data regions and dirty data regions. Redundant copies are maintained only for dirty data sectors, not for entire cache lines. This segmentation allows selective redundancy tracking at the sector level, improving data reliability for modified data while minimizing unnecessary cache space consumption.
Solution Approach 2:
Redundancy is applied locally only to dirty data sectors that require protection, rather than uniformly to all cache data. The system tracks and maintains redundant copies selectively for sectors marked as dirty, optimizing the balance between reliability improvement and cache space utilization.
2Measurement precision
If sector-based tracking is implemented, then tracking precision is improved, but device complexity increases
Solution Approach 1:
The cache address space is segmented into sectors, and tracking metadata is maintained at the sector level rather than the cache line level. This segmentation enables precise tracking of dirty data locations while using a simplified metadata structure that records only the necessary sector identifiers and dirty status flags.
Solution Approach 2:
The sector-based tracking system uses self-describing metadata structures that automatically identify dirty sectors and their locations. The tracking mechanism serves itself by maintaining compact sector-level pointers and status bits that require minimal management overhead, reducing the effective complexity despite improved precision.
3Productivity
If clean data is omitted during migration, then productivity is improved, but data completeness may be compromised
Solution Approach 1:
Clean data is extracted and excluded from the migration process. The system identifies sectors marked as clean through the sector-based tracking metadata and omits them from migration operations. Only dirty sectors that contain modified data requiring persistence are migrated, improving productivity while maintaining data completeness for all necessary information.
Solution Approach 2:
Migration operations are applied selectively only to dirty data sectors rather than uniformly to all cache lines. The sector-based tracking enables identification of which specific sectors contain dirty data, allowing the migration process to focus resources on preserving only the necessary modified data while skipping clean data, thus improving efficiency without compromising data integrity.
Data Source
AI summary
Exemplary methods, apparatuses, and systems include identifying that a first cache line from a first cache is subject to an operation that copies data from the first cache to a non-volatile memory. A first portion of the first cache line stores clean data and a second portion of the first cache line stores dirty data. A redundant copy of the dirty data is stored in a second cache line of the first cache. In response to identifying that the first cache line is subject to the operation, metadata associated with the redundant copy of the dirty data is used to copy the dirty data to a non-volatile memory while omitting the clean data.