XTorch: A High-Performance C++ Framework for Deep Learning Training

The deep learning ecosystem is predominantly driven by high-level Python frameworks like PyTorch and TensorFlow, which offer exceptional flexibility and ease of use. However, the reliance on a Python front-end can introduce significant performance overhead, partic- ularly in data-intensive training pipelines, often necessitating multi-GPU setups to achieve acceptable training times. This paper introduces XTorch, a high-level C++ deep learning framework built atop LibTorch, designed to bridge the gap between Python’s usability and C++’s raw performance. XTorch provides a familiar API for datasets, transforms, and mod- els while eliminating Python-related bottlenecks. We demonstrate its efficacy by training a Deep Convolutional Generative Adversarial Network (DCGAN) on the CelebA dataset. Our results show that XTorch, running on a single NVIDIA RTX 3090 GPU, completes a 5-epoch training run in 219 seconds. This represents a 37% speedup over a standard PyTorch implementation which required 350 seconds using two RTX 3090 GPUs with DataParallel. This work validates that a native C++ framework can not only match but significantly outperform common multi-GPU Python setups, offering a compelling case for reducing hardware costs and accelerating research and deployment.