Nested Conditional Check Instrumentation for Accurate PGO
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Solution Overview
Problem
Current profile-guided optimization (PGO) techniques for nested conditional checks are insufficient in accurately determining the execution frequency of inner conditional checks, leading to suboptimal code optimization decisions.
Innovation Solution
The method involves duplicating inner conditional checks and placing them before the original nested conditional checks in the program code, allowing for the collection of execution frequency data that is not otherwise available, thereby enhancing the accuracy of optimization decisions during the recompilation phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current PGO instrumentation techniques are used for nested conditional checks, then the instrumentation is simple to implement, but the execution frequency data collected is inaccurate
Solution Approach 1:
The patent segments the nested conditional check structure by introducing separate instrumentation for outer and inner checks. Each conditional check is instrumented independently with dedicated counters, allowing accurate measurement of execution frequencies at different nesting levels without requiring complex unified instrumentation.
Solution Approach 2:
The patent introduces intermediary basic blocks and counter variables that mediate between the nested conditional checks and the profile data collection. These intermediary elements facilitate accurate frequency measurement by capturing execution paths separately before aggregation, resolving the complexity-accuracy tradeoff.
2Productivity
If instrumentation is added to collect nested conditional check frequencies, then optimization decisions improve, but the program execution time during profiling increases
Solution Approach 1:
The patent performs preliminary instrumentation during the compilation phase, preparing counter variables and instrumentation code before execution. This allows the profiling phase to collect data efficiently without repeated compilation overhead, improving optimization quality while minimizing runtime penalty.
Solution Approach 2:
The patent changes the parameter of instrumentation granularity by selectively instrumenting only nested conditional checks rather than all program statements. This parameter optimization reduces the overhead of frequency collection while maintaining sufficient data quality for effective optimization decisions.
3Loss of information
If duplicate conditional checks are inserted for instrumentation, then execution frequency metadata is obtained, but the instrumented code size increases
Solution Approach 1:
The patent uses copying by creating lightweight duplicate basic blocks that contain only the conditional check and associated counter increment operations. These copied blocks are minimal in size compared to full function duplicates, providing necessary metadata while minimizing code size increase.
Solution Approach 2:
The patent applies partial action by instrumenting only the specific nested conditional checks that benefit from profile-guided optimization, rather than duplicating or instrumenting entire functions or modules. This selective approach obtains necessary metadata while avoiding excessive code size growth.
Data Source
AI summary
Aspects include executing a first phase that includes injecting instrumentation into program code in response to identifying an inner conditional check in the program code and running the instrumented program with a representative workload. The injecting includes duplicating the inner conditional check and placing a duplicate of the inner conditional check before a respective original nested conditional check in the program code to create an instrumented program. The instrumented program includes a plurality of basic blocks including original basic blocks and a newly added basic block that includes the duplicate of the inner conditional check. The method also includes executing a second phase that includes collecting execution frequency values from counters associated with the basic blocks to form metadata used to make optimization decisions for the program code.