Processor Mis-speculation Recovery via Dependency Tracking
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Solution Overview
Problem
Current mis-speculation recovery mechanisms in processors are inefficient, as they require discarding and re-executing all instructions after a mis-speculated load, leading to significant processing power and energy consumption, especially with increasing depths of internal buffers and issuing widths, resulting in wastage of independent data and results.
Innovation Solution
The method involves creating Load Recovery Metadata (LRM) and Dependent Recovery Metadata (DRM) to track dependencies between instructions, allowing only affected instructions that directly or indirectly consume data from the speculative load to be re-executed, rather than all instructions after the mis-speculated load, utilizing Load Dependency Data (LDD) for precise re-execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current mis-speculation recovery mechanisms discard and re-execute all instructions after a mis-speculated load, then program correctness is maintained, but processing power and energy consumption increase significantly
Solution Approach 1:
The patent segments the set of instructions following a mis-speculated load into two categories: (1) instructions that depend on the mis-speculated load and must be re-executed, and (2) independent instructions that do not depend on the mis-speculated load and can be retained. This segmentation is achieved through dependency tracking mechanisms that analyze data flow and control flow relationships, allowing the system to selectively re-execute only the necessary subset of instructions rather than discarding all subsequent instructions.
Solution Approach 2:
The patent applies local quality by treating different instructions differently based on their dependency characteristics. Instructions with dependency on the mis-speculated load receive special handling (re-execution), while independent instructions maintain their original execution results. This differential treatment optimizes the recovery process by applying correctness-restoring actions only where necessary rather than uniformly across all instructions.
2Reliability
If current mis-speculation recovery mechanisms discard and re-execute all instructions after a mis-speculated load, then program correctness is maintained, but execution time increases
Solution Approach 1:
The patent segments the instruction stream to identify only those instructions that are dependent on the mis-speculated load. By using dependency tracking information (such as register dependency chains, memory dependency chains, and control flow dependency chains), the system creates a minimal re-execution set that includes only necessary instructions, thereby reducing the time loss associated with recovery.
Solution Approach 2:
The patent performs preliminary dependency analysis and tracking during normal instruction execution, maintaining information about instruction dependencies in data structures such as dependency graphs or tracking buffers. This preliminary action prepares the system to quickly identify affected instructions during mis-speculation recovery, avoiding the need for time-consuming analysis during the recovery phase itself.
3Reliability
If current mis-speculation recovery mechanisms discard and re-execute all instructions after a mis-speculated load, then program correctness is maintained, but productivity decreases due to wastage of independent data and results
Solution Approach 1:
The patent segments instructions into dependent and independent categories, preserving the results of independent instructions while re-executing only dependent instructions. This segmentation maintains program correctness for the affected portion while preserving productive work in the independent portion, thereby improving overall productivity during mis-speculation recovery.
Solution Approach 2:
The patent selectively discards only the results of instructions that depend on the mis-speculated load, while recovering and retaining the results of independent instructions. This selective discarding and recovering approach minimizes waste of computational resources and maintains productivity by preserving useful work that is not affected by the mis-speculation.
Data Source
AI summary
Embodiments provide methods and apparatus for facilitating a memory mis-speculation recovery. The method includes detecting a speculative load and a set of speculative load dependent instructions and creating a Load Recovery Metadata (LRM) including an information for instruction re-execution for the speculative load. The method includes creating a set of Dependent Recovery Metadata (DRM). Each DRM of the set of DRM corresponds to each speculative load dependent instruction. A DRM includes an information for instruction re-execution for a speculative load dependent instruction. The method includes creating a Load Dependency Data (LDD) including information of the LRM and information of the set of DRM. The method includes detecting the speculative load as a mis-speculated load. The method includes re-executing the mis-speculated load and the set of speculative load dependent instructions. The re-execution includes utilizing the LDD to fetch the information of the LRM and the information of the set of DRM.