Runtime Profile-Guided GPU Compilation via Versioned Data
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Solution Overview
Problem
Profile guided optimization (PGO) for CPU-based systems typically generates profiles in an offline test environment, which may not accurately reflect real-time execution conditions, leading to suboptimal performance improvements, especially when application or GPU versions change, as it does not account for dynamic workloads and varying usage patterns.
Innovation Solution
Implementing runtime PGO that generates profiles during actual application execution, allowing for dynamic compilation decisions based on workload characteristics, and managing profile data through versioning and targeted collection to reduce redundancy, with opportunities for performance improvements even when profiling is incomplete, and using cloud services for compilation to minimize overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If offline test environment profiling is used for PGO, then compilation can be performed in advance, but the profile data does not accurately reflect real-time execution conditions leading to suboptimal performance
Solution Approach 1:
The system performs preliminary profiling during offline test environments to generate initial profile data before production deployment. This preliminary action provides a baseline optimization while the system is designed to adapt and refine profiles during actual runtime execution, combining advance preparation with real-time adjustment capabilities.
Solution Approach 2:
The system transitions from static offline profiling to dynamic runtime profiling, where profile data is continuously updated and refined during actual execution. This dynamic approach allows the system to adapt to changing workload characteristics, application versions, and GPU states, ensuring profile accuracy reflects real-time conditions.
2Manufacturing precision
If runtime profiling is implemented to capture real-time execution conditions, then performance optimization accuracy improves, but system overhead increases
Solution Approach 1:
The system implements targeted profiling that focuses collection efforts on specific profile events and characteristics most relevant to performance optimization. Rather than uniformly profiling all execution aspects, it selectively gathers data based on workload type, application characteristics, and identified performance bottlenecks, reducing overhead while maintaining optimization accuracy.
Solution Approach 2:
The system employs incremental profiling where complete profile data collection is performed only when necessary, while partial profiling is used for routine optimizations. It balances between gathering sufficient data for accurate optimization and minimizing the overhead burden, using thresholds and heuristics to determine when full profiling is warranted versus when lighter profiling suffices.
3Adaptability or versatility
If profile data is collected for all application versions, then comprehensive coverage is achieved, but data redundancy increases
Solution Approach 1:
The system uses versioning parameters to track and differentiate profile data across application and GPU versions. When detecting version changes, it selectively collects new profile data only when necessary, comparing against existing profiles to identify significant deviations. This parameter-based approach maintains comprehensive version coverage while avoiding redundant collection of unchanged profile characteristics.
Solution Approach 2:
The system discards redundant profile data by identifying and eliminating duplicate or superseded profiles across versions. It maintains only the necessary subset of profile data that provides unique optimization value for each version combination, recovering and reusing applicable profile information across similar workloads and versions to reduce overall data volume while preserving adaptability.
4Manufacturing precision
If complete profiling is performed before compilation, then optimal code generation is achieved, but compilation time and resource usage increase
Solution Approach 1:
The system performs partial profiling by collecting only the most critical profile events and characteristics needed for effective optimization, rather than gathering complete execution traces. It uses sampling techniques and event filtering to capture sufficient information for high-quality code generation while significantly reducing the data processing burden during compilation, thereby improving compilation efficiency without sacrificing code quality.
Solution Approach 2:
The system extracts and separates essential profile information from complete execution data, isolating only the key metrics and patterns needed for optimization decisions. This extraction process removes unnecessary detailed trace information, retaining only the condensed essential data required for high-quality code generation, thus reducing compilation resource requirements while maintaining optimization effectiveness.
Data Source
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AI summary
Examples described herein relate to technologies to execute a compiler for a process to be executed by one or more graphics processing units (GPUs) to compile the process based on run-time profile guided optimization (PGO). In some examples, the process is compiled based on run-time PGO is based on profile data versioned by application, driver, and GPU version; previously generated profile data; a subset of draws to profile and optimize; or other factors.