Simulator Trace Checksums for Instruction Deviation Debugging
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Solution Overview
Problem
Debugging simulation errors in computer systems is challenging due to the complexity of simulating hundreds of billions of instructions, making traditional instruction tracing impractical and time-consuming.
Innovation Solution
A method involving generating two separate simulations and performing a trace operation to identify instruction execution deviations by comparing checksum values at a reporting frequency, using instruction count and checksums to pinpoint the deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional instruction tracing is used to debug simulation errors, then debugging accuracy is improved, but execution time and computational resources increase significantly
Solution Approach 1:
The patent segments the debugging process by dividing the simulation into multiple checkpoints and using shadow execution to track state changes at these segments. Instead of tracing every single instruction, the system divides the execution into manageable portions and compares states at segment boundaries, significantly reducing the time overhead while maintaining debugging accuracy.
Solution Approach 2:
The patent introduces shadow execution as an intermediary mechanism that runs parallel to the main simulation. This shadow execution acts as a mediator that tracks state changes without interfering with the primary simulation flow, allowing for efficient comparison and error detection without the full overhead of traditional instruction-by-instruction tracing.
2Reliability
If traditional instruction tracing is performed on hundreds of billions of instructions, then complete debugging coverage is achieved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the hundreds of billions of instructions into smaller execution blocks with intermediate checkpoints. The shadow execution mechanism tracks state changes at these checkpoints rather than continuously, reducing computational complexity while maintaining comprehensive debugging coverage through strategic sampling of critical states.
Solution Approach 2:
The patent implements partial action by performing shadow execution and state comparison only at selected checkpoints rather than on every single instruction. This partial monitoring approach provides sufficient debugging coverage to detect errors in large-scale simulations without the prohibitive computational complexity of complete instruction-by-instruction tracing.
3Measurement precision
If checksum comparison is performed at every instruction, then deviation detection precision is improved, but execution speed decreases
Solution Approach 1:
The patent implements periodic action by performing checksum comparison at regular intervals or at specific checkpoints during simulation execution rather than at every instruction. This periodic verification maintains the ability to detect deviations with sufficient precision while preserving execution speed by reducing the frequency of comparison operations.
Solution Approach 2:
The patent uses partial action by performing full checksum comparison only at selected checkpoints rather than continuously. Between checkpoints, the shadow execution tracks state changes more efficiently with reduced overhead, achieving a balance between deviation detection precision and execution speed through selective application of the comparison operation.
Data Source
AI summary
A method to debug instruction execution errors in a simulated computer system is provided. The method includes generating two separate simulations of the same system and causing a code including a set of instructions to execute on the two separate simulations. The computer implemented method further includes performing an efficient trace operation starting from a start instruction to an end instruction of the set of instructions on the two separate simulations. When trace operation is performed, an instruction execution deviation is identified between the code executed in the two separate simulations by comparing checksum values at a reporting frequency, determining that the comparison of the checksum values indicates a mismatch, and using instruction count and the reporting frequency to capture at least one instruction leading up to the instruction execution deviation.