CUDA, TENSORRT AND CUDNN : UNDERSTANDING NVIDIAS ML ACCELERATION STACK
Srinivas Kola · 2025
The rapidly growing demand for computationally efficient machine learning (ML) workflows has significantly accelerated the adoption of hardware-accelerated processing units, with Graphics Processing Units (GPUs) emerging as the predominant choice for both research and production environments.Among the available solutions, NVIDIA's ML acceleration stack-comprising CUDA, cuDNN, and TensorRT-serves as a cornerstone for enabling high-throughput, low-latency training and inference on NVIDIA GPU architectures.Collectively, these components provide a tightly integrated software ecosystem that abstracts hardware complexity while exposing fine-grained control for performance-critical ML tasks.In this work, we present a comprehensive and systematic analysis of the individual elements within this stack, the interdependencies between them, and their collective contribution to optimizing the end-to-end ML pipeline.We first examine CUDA, the parallel computing platform and programming model that enables direct interaction with the GPU's computational cores, detailing its architecture, memory hierarchy, kernel execution model, and API surface.Particular attention is given to its role in orchestrating GPU resource allocation, thread management, and interconnect utilization, all of which are essential for achieving deterministic and scalable performance.CUDA, TensorRt and cuDNN : understanding Nvidias ML acceleration stack https://iaeme.com/Home/