Code Instruction Substitution for Lower Energy Consumption
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Solution Overview
Problem
Existing software implementations consume excessive energy due to inefficient coding practices, leading to increased carbon footprint and resource usage, despite sufficient computing resources being available.
Innovation Solution
Implementing rules to analyze and automatically suggest or execute changes in code to replace less energy-efficient instructions with more efficient alternatives, utilizing containerized environments for accurate measurement and parallelization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If standard coding practices are used, then software functionality is achieved, but energy consumption increases
Solution Approach 1:
The patent changes the operational parameters of code instructions by identifying and replacing standard instructions with more energy-efficient alternatives. The system analyzes code at the instruction level and substitutes operations that consume fewer computational resources while maintaining the same functional output, directly addressing the energy consumption issue without sacrificing productivity.
Solution Approach 2:
The patent substitutes less efficient computational mechanisms with more efficient ones. By replacing standard programming instructions with optimized alternatives, the system effectively substitutes the computational 'mechanics' to reduce energy usage while preserving the intended software functionality.
2Use of energy by moving object
If code is analyzed and modified for energy efficiency, then energy consumption decreases, but system complexity increases
Solution Approach 1:
The patent implements a self-service approach where the code analysis and optimization system automatically identifies and replaces inefficient instructions without requiring manual intervention. The system serves itself by autonomously analyzing its own codebase, applying optimization rules, and generating optimized code, thereby reducing the perceived complexity for end users while maintaining the sophisticated optimization capabilities.
Solution Approach 2:
The patent introduces an intermediary optimization layer that sits between the original code and execution. This intermediary system handles the complexity of energy efficiency analysis and instruction replacement, shielding users from the underlying complexity while delivering energy-optimized code. The intermediary manages rule application, energy measurement, and code transformation transparently.
3Measurement precision
If energy measurement is performed in containerized environments, then measurement accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-configuring containerized environments for energy measurement before actual code execution. By setting up the measurement infrastructure in advance and parallelizing the measurement process across multiple containers, the system achieves accurate energy measurements without significantly increasing overall processing time, as the measurement setup is prepared beforehand rather than during execution.
Data Source
AI summary
Techniques and solutions are provided for increasing the energy efficiency of computing code. Many computing operations can be implemented in a number of different ways. While the end result of each implementation may be the same, the energy efficiency of the implementations can vary dramatically. Disclosed techniques provide rules that can be used to analyze code for a particular implementation of an operation. If a rule is triggered, a recommendation to replace the implementation with a more energy efficient implementation can be provided, or the code can automatically be changed to include the more energy efficient implementation. Techniques for defining rules are also provided, such as by measuring or estimating energy used by various ways of implementing an operation. Measurement or estimation of energy used during code execution can be performed in a containerized environment, such as to provide improved accuracy, and allow for parallelization.