BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems

2025-03-18

AI Summary

To address inefficient backpropagation and excessive memory overhead in small-scale computational graph training on single-CPU nodes, this paper proposes BurTorch, a lightweight training framework. Methodologically, BurTorch abandons the abstraction-layer bloat of general-purpose deep learning frameworks and instead embraces the fundamentals of compiled-language programming and numerical optimization: it employs hand-optimized C++, static computational graphs, explicit memory management, and a native backpropagation engine grounded in the Linnainmaa/Rumelhart chain rule. This system-level design directly targets performance bottlenecks inherent to small-graph training. Experiments demonstrate that, on representative small-graph tasks, BurTorch achieves up to 2000× speedup and 3500× memory reduction over PyTorch; even on a micro-scale GPT-3 model, it delivers 20× acceleration and 80× memory savings. These results significantly advance efficient, resource-constrained deep learning training.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

In this work, we introduce BurTorch, a compact high-performance framework designed to optimize Deep Learning (DL) training on single-node workstations through an exceptionally efficient CPU-based backpropagation (Rumelhart et al., 1986; Linnainmaa, 1970) implementation. Although modern DL frameworks rely on compilerlike optimizations internally, BurTorch takes a different path. It adopts a minimalist design and demonstrates that, in these circumstances, classical compiled programming languages can play a significant role in DL research. By eliminating the overhead of large frameworks and making efficient implementation choices, BurTorch achieves orders-of-magnitude improvements in performance and memory efficiency when computing $ abla f(x)$ on a CPU. BurTorch features a compact codebase designed to achieve two key goals simultaneously. First, it provides a user experience similar to script-based programming environments. Second, it dramatically minimizes runtime overheads. In large DL frameworks, the primary source of memory overhead for relatively small computation graphs $f(x)$ is due to feature-heavy implementations. We benchmarked BurTorch against widely used DL frameworks in their execution modes: JAX (Bradbury et al., 2018), PyTorch (Paszke et al., 2019), TensorFlow (Abadi et al., 2016); and several standalone libraries: Autograd (Maclaurin et al., 2015), Micrograd (Karpathy, 2020), Apple MLX (Hannun et al., 2023). For small compute graphs, BurTorch outperforms best-practice solutions by up to $ imes 2000$ in runtime and reduces memory consumption by up to $ imes 3500$. For a miniaturized GPT-3 model (Brown et al., 2020), BurTorch achieves up to a $ imes 20$ speedup and reduces memory up to $ imes 80$ compared to PyTorch.

Problem

• Optimizes deep learning training on single-node workstations.
• Reduces runtime and memory overheads in CPU-based backpropagation.
• Outperforms existing frameworks in small compute graph scenarios.

Innovation

• Efficient CPU-based backpropagation implementation
• Minimalist design using classical compiled languages
• Significant runtime and memory efficiency improvements