Offline Memoization Analysis for Function Selection
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Solution Overview
Problem
Memoization techniques face overhead challenges due to cache testing and result storage, which can impact performance in computationally expensive function calls, and existing methods lack efficient offline analysis for identifying optimal memoizable functions.
Innovation Solution
An offline memoization optimization mechanism that monitors executable code and performs offline analysis to identify functions for memoization, storing results in a configuration file or database, which is then used by the execution environment to speed up performance without altering the target code, and includes cost/benefit optimization to prioritize functions with the highest benefit within cache constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If memoization is implemented by caching function results, then computation time is reduced for repeated function calls, but overhead is added due to cache testing and result storage
Solution Approach 1:
The patent performs offline analysis before runtime to pre-identify which functions are suitable for memoization. By analyzing profiling data, call frequencies, and computational costs in advance, the system creates a configuration that guides runtime memoization decisions, avoiding the need for complex runtime analysis while still achieving optimal performance improvements.
Solution Approach 2:
The system uses automated profiling and offline analysis tools to self-determine which functions should be memoized, rather than requiring manual configuration or complex runtime decision-making. The offline analyzer automatically evaluates function characteristics and generates optimization recommendations, reducing the burden on the runtime system.
2Productivity
If comprehensive offline analysis is performed to identify optimal memoizable functions, then memoization effectiveness is improved, but analysis complexity and resources increase
Solution Approach 1:
The offline analysis process is divided into distinct phases: profiling data collection, function characteristic analysis, cost-benefit evaluation, and configuration generation. Each phase processes specific aspects of function behavior independently, making the overall complex analysis manageable and modular while still achieving comprehensive optimization insights.
3Quantity of substance
If cache space is limited, then memory usage is controlled, but the number of functions that can be memoized is reduced
Solution Approach 1:
The offline analysis evaluates multiple parameters including call frequency, computational cost, and input variability to prioritize which functions provide the greatest benefit for memoization. By changing the selection criteria from simple frequency-based to a multi-parameter cost-benefit analysis, the system maximizes the impact of limited cache space on overall performance.
Data Source
AI summary
Memoization may be deployed using a configuration file or database that identifies functions to memorize, and in some cases, includes input and result values for those functions. The configuration file or database may be created by profiling target code and offline or otherwise separate analysis of the profiling results. The configuration file may be used by an execution environment to identify which functions to memorize during execution. The offline or separate analysis of the profiling results may enable more sophisticated analysis than could otherwise be performed in parallel with executing the target code, including historical analysis of multiple instances of the target code and sophisticated cost/benefit analysis.