Compiler Optimization Profiles for Data Center Workloads
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Solution Overview
Problem
Existing compiler technologies lack efficiency in compiling computer programs, particularly for frequently executed machine-learning models, as they do not effectively utilize optimization profiles to improve compilation performance without incurring significant overhead or disrupting ongoing workloads.
Innovation Solution
A system generates and maintains optimization profiles for frequently used computer programs, allowing the compiler to use pre-generated profiles that define specific settings, thereby improving compilation efficiency without interrupting existing workloads and adding minimal overhead, and allows for the addition of new profiles via change lists for version control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optimization profiles are generated and applied to improve compilation efficiency, then compilation speed and performance are improved, but system complexity and overhead increase
Solution Approach 1:
Optimization profiles are generated in advance through background auditing and analysis of compiler behavior, then stored for later use. This preliminary action allows the compiler to directly apply pre-computed optimization settings without performing complex analysis during compilation, thus improving compilation efficiency while avoiding the overhead of real-time optimization profile generation.
Solution Approach 2:
The system creates copies of optimization profiles that capture compiler behavior at specific points in time. These profile copies can be stored, retrieved, and applied without modifying the original compiler or requiring the full complexity of the optimization analysis process to be repeated during compilation.
2Productivity
If optimization profiles are generated during compilation, then compilation efficiency is improved, but existing workloads are interrupted
Solution Approach 1:
Optimization profiles are generated in advance through background auditing processes that run independently of active compilation workloads. The auditing phase analyzes compiler behavior and generates profiles without interfering with ongoing compilations. Once generated, these profiles are stored and can be applied to future compilations, thus improving efficiency while maintaining workload continuity.
Solution Approach 2:
The system separates the optimization profile generation process from the compilation process itself. The auditing and profile generation occur as distinct background operations, while compilation workloads proceed independently. This segmentation allows both processes to run concurrently without interfering with each other, ensuring workload continuity while still enabling efficiency improvements.
3Loss of time
If optimization profiles are used to reduce compilation overhead, then compilation time is reduced, but profile validity and accuracy may deteriorate over time
Solution Approach 1:
The system implements periodic auditing of optimization profiles to verify their continued validity. At scheduled intervals, the system re-audits profiles against current compiler behavior and workloads, updating or regenerating profiles as needed. This periodic validation ensures that profiles remain accurate and effective over time while still allowing most compilations to use the cached profiles for reduced overhead.
Solution Approach 2:
The system incorporates feedback mechanisms where compilation performance data is collected and used to validate whether optimization profiles are still effective. If profiles are found to be outdated or ineffective, the feedback triggers regeneration of updated profiles. This closed-loop feedback ensures profile accuracy is maintained while minimizing the frequency of full profile regeneration.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for feedback-directed optimization. One of the methods includes maintaining a data store comprising a plurality of optimization profiles that are used by a compiler to compile respective computer programs. The computer programs can be invoked by a set of executing workloads. Operations are repeatedly performed that include, for each optimization profile in at least a subset of the optimization profiles: determining or predicting whether the optimization profile is a valid optimization profile for a current software version of the compiler, and in response to determining or predicting that the optimization profile is not a valid optimization profile for the current software version of the compiler, removing the optimization profile from the data store.