Deploying a trained neural network is a nuanced task that extends beyond the simple translation of operators into GPU kernels. According to NVIDIA, the process is fundamentally a compilation challenge that involves multiple variables and potential configurations.
When NVIDIA TensorRT constructs an engine, it must navigate a series of interdependent decisions. This complex procedure, referred to as scheduling, includes graph-IR transformations, operator fusion, implementation selection, memory placement, and quantization strategies. Each of these steps plays a critical role in the final performance of the deployed model on GPU hardware.
To address these challenges, NVIDIA has introduced its Global Performance Tuner. This tool is designed to move optimization beyond traditional single-build routes, allowing developers to better manage the intricate balance of parameters required for efficient neural network execution. By refining how these compilation choices are made, the tuner aims to provide more robust solutions for AI infrastructure deployment.
Source: NVIDIA
Original article: https://developer.nvidia.com/blog/scaling-nvidia-tensorrt-performance-optimization-beyond-a-single-build-route-with-the-global-performance-tuner-part-1/
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