GPU Kernel Tuning for Shape-Aware On-Device Compilation
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Solution Overview
Problem
Existing graphics processing unit (GPU) compilation techniques struggle to optimize kernel execution efficiently due to varying kernel shapes, leading to suboptimal performance.
Innovation Solution
A system and method for on-device kernel tuning that utilizes characteristics of the kernels to make accurate and efficient compilation decisions, enhancing GPU performance by optimizing kernel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If kernels are compiled for specific shapes, then execution efficiency is improved, but adaptability to varying kernel shapes deteriorates
Solution Approach 1:
The system dynamically determines kernel characteristics and adjusts compilation decisions based on actual kernel properties rather than using fixed compilation strategies. This allows the compilation process to adapt to varying kernel shapes in real-time, resolving the contradiction between optimization for specific shapes and adaptability to varying shapes.
Solution Approach 2:
The system changes compilation parameters based on determined kernel characteristics such as shape, compute intensity, and memory access patterns. By adjusting compilation parameters dynamically according to kernel properties, the system achieves both efficient execution for specific kernel types and adaptability to varying kernel shapes.
2Speed
If compilation is optimized for specific kernel shapes, then processing speed is improved, but compilation complexity increases
Solution Approach 1:
The system performs self-service by automatically determining kernel characteristics and making compilation decisions without requiring external intervention or complex manual configuration. This automation reduces compilation complexity while maintaining optimized processing speed through characteristic-based compilation decisions.
Solution Approach 2:
The system uses feedback from kernel characteristic determination to guide compilation decisions. By analyzing kernel properties and using this information to adjust compilation parameters, the system achieves optimized processing speed while keeping compilation complexity manageable through systematic feedback-driven decision-making.
3Ease of operation
If general compilation strategies are used, then ease of operation is improved, but execution performance deteriorates
Solution Approach 1:
The system performs preliminary determination of kernel characteristics before compilation, allowing optimization decisions to be made based on actual kernel properties. This preliminary analysis maintains ease of operation by automating the process while significantly improving execution performance through characteristic-based optimization.
Data Source
AI summary
Tuning of kernels for on device compilation is described. An example of an apparatus includes a computer memory to store data for processing; and processing resources including a GPU, the GPU including compilation circuitry, wherein the compilation circuitry includes kernel evaluation circuitry to evaluate compute kernel received for compilation and to determine one or more characteristics of the compute kernel, and device compiler circuitry to support compilation of the compute kernel, wherein the device compiler circuitry is to tune the compilation of the compute kernel based at least in part on the one or more characteristics of the compute kernel.