Compiler Optimization Hooks for Hardware-Specific Code Tuning
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Solution Overview
Problem
Existing methods for optimizing high-level computer code are manual, time-consuming, and inadequate for high-performance computing environments, failing to account for specific hardware and use-cases.
Innovation Solution
A computer-implemented method that automatically generates specialized high-performance programs from a single source code by inserting optimization hooks, executing the code with predefined numerical values, evaluating performance metrics, and iteratively refining the code to achieve optimal execution speed and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual optimization methods are used, then code can be optimized for specific hardware and use-cases, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service optimization by automatically generating multiple specialized programs from a single source code through iterative compilation and performance evaluation. The optimization system performs timing, testing, and maintenance operations autonomously, eliminating the need for manual optimization exercises while achieving hardware-specific and use-case-specific optimizations.
Solution Approach 2:
The system creates multiple copies of the source code, each compiled with different optimization hooks and parameters to generate specialized programs for different hardware and use-cases. This copying approach allows parallel exploration of optimization paths without manual intervention, significantly reducing optimization time while maintaining precision.
2Adaptability or versatility
If multiple versions of code are maintained for different hardware and use-cases, then optimization for specific platforms is achieved, but maintenance complexity increases
Solution Approach 1:
The system implements universality by taking a single source code and generating multiple specialized versions through automated compilation with different optimization hooks. This single source code serves multiple functions by producing hardware-specific and use-case-specific optimizations, eliminating the need to maintain separate code versions for different platforms while preserving adaptability.
Solution Approach 2:
The system employs feedback mechanisms where performance metrics from executing compiled code are evaluated and fed back into the optimization process. This feedback loop automatically adjusts optimization parameters and generates improved versions, reducing maintenance complexity by automating the iteration process while maintaining versatility across different hardware and use-cases.
3Productivity
If automated optimization systems are implemented, then optimization time is reduced, but the system complexity increases
Solution Approach 1:
The optimization system is segmented into distinct functional components: source code reception, optimization hook insertion, compilation, execution, performance evaluation, and iterative refinement. This segmentation allows each component to perform its specific function independently, managing system complexity while achieving high productivity through automated optimization workflows.
Data Source
AI summary
A computer implemented method for compiling a computer program, the method including receiving a source code for a computer program, use-case data, and user-selected operating parameters, inserting optimization hook code into the source code based upon identified code loops, compiling source code with optimization hook code, executing the compiled code with optimization hooks code, wherein the optimization hooks of the optimization hook code are set to an initial value, receiving intermediate code based upon the executing, compiling the received intermediate code, executing the compiled received intermediate code using the use-case data, evaluating the executing the compiled received intermediate code using the use-case data based upon performance metrics, identifying code associated with preferential performance metrics, and compiling the identified code into a runnable computer program.