Piecemeal Profiling for Low-Overhead Program Optimization
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Solution Overview
Problem
Obtaining profile information in live production environments, such as high-performance gaming systems, is challenging due to performance overhead, making it difficult to implement profile-guided optimizations effectively.
Innovation Solution
A piecemeal profiling system that collects lightweight, approximate profiles across multiple frames or devices, aggregating them to generate whole program profiles for determining profile-guided optimization opportunities, which can be implemented on user computing devices or a server, without significant user effort or performance impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional profiling is performed in live production environments, then profile information can be obtained for optimization, but performance overhead increases significantly
Solution Approach 1:
The patent divides the profiling process into segments by collecting profile information from multiple individual frames separately rather than profiling the entire program at once. Each frame is profiled independently, and the results are aggregated to form a complete program profile. This segmentation reduces the performance overhead of any single frame while still providing comprehensive profiling data across the entire application.
Solution Approach 2:
The patent applies partial action by collecting profile information from a subset of frames (e.g., every Nth frame or a random sample of frames) rather than profiling every single frame. This partial sampling approach provides sufficient profile information for optimization while significantly reducing the performance overhead compared to complete continuous profiling.
2Measurement precision
If profile information is collected frequently to improve optimization accuracy, then measurement precision improves, but performance overhead increases
Solution Approach 1:
The patent implements periodic action by collecting profile information at regular intervals (e.g., every Nth frame) or at periodically selected frames rather than continuously. This periodic sampling provides adequate profile data for accurate optimization while minimizing the time lost to profiling operations, as the profiler remains inactive during intervals between sampling points.
3Loss of information
If comprehensive program profiling is performed, then complete optimization opportunities are identified, but device complexity increases
Solution Approach 1:
The patent merges profile information from multiple individual frame profiles into a unified complete program profile. By aggregating the segmented frame-level profiles, the system reconstructs comprehensive program-wide profile data without requiring a complex monolithic profiling system. The merging process combines simpler individual profiles to achieve complete optimization information.
Data Source
AI summary
Profile-guided optimization is a technique for optimizing execution of computer programs using profile information to improve program runtime performance. Obtaining the profile information can be challenging, especially in live production environments such as high-performance gaming systems. A profiling strategy is provided herein that obtains profile information without requiring extra effort from users. The profiling strategy collects several approximate, lightweight profiles called piecemeal profiles over one or more lifetimes of a computer program, or application. The piecemeal profiles are then used to generate whole program application profiles that can then be used to improve the execution of the application. A piecemeal profile is profile information of a section or portion of an application.