Executable Code Version Selection for Runtime Optimization
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Solution Overview
Problem
Current optimization techniques for executable code often degrade performance when executed on different processor types or under varying runtime conditions such as core utilization and temperature, leading to suboptimal application performance.
Innovation Solution
A system that selectively executes different versions of executable code optimized for specific conditions by evaluating test conditions such as core utilization, processor temperature, and processor type, using a new conditional branch instruction to redirect control flow and choose the appropriate code version at runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If executable code is optimized for a specific processor type, then performance on that processor type is improved, but performance on other processor types degrades
Solution Approach 1:
The executable code is segmented into multiple versions, each optimized for specific processor types or runtime conditions. The system divides the codebase into alternative implementations that can be selectively executed based on the detected environment, resolving the contradiction between optimization for specific processors and compatibility across different processors.
Solution Approach 2:
The system dynamically selects which version of the executable code to run based on runtime conditions such as processor type, core utilization, and temperature. This dynamic adaptation allows the application to maintain optimal performance across varying hardware configurations without requiring separate compiled binaries for each processor type.
2Productivity
If aggressive speculation optimization is employed, then performance on lightly utilized cores is improved, but performance degrades when cores are heavily utilized due to excessive resource consumption
Solution Approach 1:
The system dynamically adjusts the speculation optimization level based on detected core utilization. When cores are lightly utilized, aggressive speculation is enabled to maximize performance. When cores are heavily utilized, the system switches to a more conservative optimization version that consumes fewer processor resources, thereby maintaining stable performance across varying load conditions.
Solution Approach 2:
The system changes optimization parameters (such as speculation aggressiveness) based on runtime conditions. By detecting core utilization levels and switching between different code versions with different optimization parameters, the system resolves the contradiction between achieving high performance under light load and maintaining stable performance under heavy load.
3Temperature
If clock-frequency is reduced or cores are disabled to manage processor temperature, then thermal issues are mitigated, but application performance degrades
Solution Approach 1:
The system prepares multiple versions of executable code in advance, with different optimization characteristics. When thermal issues are detected, the system can immediately switch to a version optimized for thermal efficiency without needing to reduce clock-frequency or disable cores, thus mitigating thermal issues while maintaining acceptable performance.
Solution Approach 2:
The system changes execution parameters by switching between different code versions optimized for different thermal conditions. This allows the system to manage processor temperature through software optimization rather than hardware throttling, thereby avoiding performance degradation from frequency reduction or core disabling.
4Productivity
If multiple differently optimized code versions are maintained, then optimal performance under different conditions is achieved, but system complexity increases
Solution Approach 1:
The system creates a universal executable code module that can execute multiple differently optimized versions of the same source code. This multi-functional design allows a single binary to adapt to various processor types and runtime conditions, achieving optimal performance across conditions while presenting a simple unified interface to the user.
Data Source
AI summary
One embodiment of the present invention provides a system that selectively executes different versions of executable code for the same source code. During operation, the system first receives an executable code module which includes two or more versions of executable code for the same source code, wherein the two or more versions of the executable code are optimized in different ways. Next, the system executes the executable code module by first evaluating a test condition, and subsequently executing a specific version of the executable code based on the outcome of the evaluation, so that the execution is optimized for the test condition.