Indirect Prefetcher Using Commit-Stage Array Dependency Tracking
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Solution Overview
Problem
Existing prefetchers struggle to accurately and efficiently handle irregular, array-indirect accesses in cloud workloads, leading to increased memory latency and power consumption, while also posing security risks due to out-of-order training and inadequate consideration of diverse workload types.
Innovation Solution
A hardware prefetcher architecture that identifies array-indirect relationships at the commit stage, using a producer-consumer pair model with a relationship table to store prefetch confidence, minimizing power consumption and ensuring security by throttling training and confidence measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prefetchers are used to handle irregular array-indirect accesses, then prefetching operations can be performed, but accuracy is poor and power consumption increases
Solution Approach 1:
The system performs preliminary identification of producer-consumer pairs at the commit stage, storing relationships in a relationship table before actual prefetching operations. This preliminary action enables accurate prefetching only when relationships are confirmed, avoiding wasted power on inaccurate prefetches.
Solution Approach 2:
The system dynamically adjusts prefetching behavior based on identified relationships, changing the operational parameters from generic prefetching to relationship-specific prefetching. This parameter change improves accuracy by targeting only proven producer-consumer pairs while reducing power consumption by avoiding unnecessary prefetches.
2Productivity
If existing prefetchers perform prefetching operations, then some performance improvement may be achieved, but memory latency remains high due to inaccurate prefetching
Solution Approach 1:
The system uses feedback from the commit stage to identify actual producer-consumer relationships and update the relationship table. This feedback mechanism ensures that prefetching operations are based on confirmed relationships, reducing memory latency by avoiding failed prefetches while improving productivity through accurate prefetching.
Solution Approach 2:
By performing relationship identification at the commit stage before execution, the system preliminarily determines which prefetches will be accurate. This reduces memory latency by ensuring prefetches are based on confirmed relationships while improving instructions per cycle through targeted prefetching.
3Speed
If prefetching is performed without adequate security considerations, then prefetching speed may be improved, but security risks increase due to out-of-order training
Solution Approach 1:
The system performs relationship identification at the commit stage, which is a predetermined secure point in the pipeline, before execution. This preliminary action at a secure stage prevents out-of-order training security risks while maintaining prefetch speed by having relationships ready for immediate use.
Solution Approach 2:
The relationship table acts as an intermediary that stores confirmed producer-consumer relationships. This intermediary structure separates the secure commit stage from the execution stage, allowing fast prefetching while maintaining security by only storing relationships confirmed at the secure commit boundary.
4Adaptability or versatility
If existing prefetchers are used, then general prefetching can be performed, but they fail to capture irregular array-indirect access patterns
Solution Approach 1:
The system segments the prefetching problem into two parts: regular accesses handled by conventional prefetchers and irregular array-indirect accesses handled by the relationship-based prefetching. This segmentation improves access pattern recognition for irregular accesses while maintaining versatility across different workload types.
Solution Approach 2:
The system applies different prefetching strategies to different access patterns: conventional methods for regular accesses and relationship-based methods for irregular array-indirect accesses. This local quality approach improves precision for specific access patterns while maintaining broad workload coverage.
Data Source
AI summary
Disclosed is a prefetcher, e.g., of a system with one or more cores. The prefetcher determines data dependency access (DDA) patterns, such as array indirect access, and prefetches data based on the DDA patterns.