GPU Kernel Parameter Tuning via Hardware Counter Feedback
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Solution Overview
Problem
Existing GPU parameter tuning techniques rely on heuristic or experimental methods that do not consider the underlying system structure, making it challenging to achieve optimal performance, as they often use black box approaches or extensive manual experimentation.
Innovation Solution
A method and system that combine deterministic and heuristic techniques to tune GPU parameters by monitoring hardware and performance counters, representing parameters as optimization problems, and dynamically adjusting them to optimize execution time and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If heuristic techniques are used for GPU parameter tuning, then the tuning process can be automated, but the system does not consider the underlying hardware structure leading to suboptimal performance
Solution Approach 1:
The patent introduces performance counters and hardware monitoring mechanisms as intermediaries between the heuristic tuning algorithm and the GPU hardware. These counters capture actual hardware behavior (memory accesses, compute operations, resource utilization) and feed this structured data back to the tuning algorithm, enabling it to make informed decisions that respect the underlying hardware architecture while maintaining automation.
Solution Approach 2:
The system implements a feedback loop where performance counters continuously monitor GPU execution metrics, and this feedback is used to iteratively refine parameter tuning decisions. The heuristic algorithm adjusts parameters based on observed performance patterns from the counters, creating a closed-loop system that adapts to actual hardware behavior rather than relying solely on predetermined heuristics.
2Manufacturing precision
If deterministic techniques with extensive experimentation are used, then optimal parameter values can be found, but the process requires extensive manual observation and experimentation
Solution Approach 1:
The patent performs preliminary characterization of GPU kernels by analyzing performance counter data before the actual parameter tuning process. This preliminary action identifies which hardware counters are most relevant to each kernel type, pre-computing this information so that the subsequent tuning process can focus only on the critical parameters and counters, significantly reducing the experimentation time required.
Solution Approach 2:
The tuning process is segmented into distinct phases: (1) kernel characterization using performance counters, (2) identification of critical counters, (3) parameter space exploration, and (4) optimization. This segmentation allows the system to efficiently allocate resources to each phase and avoid unnecessary experimentation in later stages once optimal parameters are identified.
3Loss of information
If comprehensive monitoring of all hardware counters is performed, then complete system understanding is achieved, but the complexity and overhead of data collection increases
Solution Approach 1:
The patent extracts and focuses only on the subset of hardware counters that are most relevant to GPU kernel performance, rather than monitoring all available counters. By analyzing performance patterns and identifying critical counters specific to each kernel type, the system extracts only the essential data needed for effective tuning, reducing monitoring complexity while maintaining complete understanding of the performance-critical aspects.
Data Source
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AI summary
This disclosure relates generally to method and system for tuning graphics processing unit (GPU) parameters of a GPU kernel .The disclosure proposes a combination of both heuristic and deterministic techniques for tuning GPU parameters of a GPU kernel to achieve optimal configuration of the GPU parameters. The proposed method and a system for tuning GPU parameters is based on deterministic techniques and heuristic techniques that includes capturing behavior of the GPU application by monitoring several GPU hardware counters that comprise several hardware resources and performance counters. The proposed tuning GPU parameters also implements a set of heuristic techniques to decide course of the tuning for various GPU parameters based on the captured behaviour of the GPU hardware counters.