解决AI代码生成中GPU编译的延迟问题，实现10-100倍提速。
Theoretical Foundations of GPU-Native Compilation for Rapid Code Iteration
- 采用GPU原生编译技术，消除CPU-GPU数据传输瓶颈。
- 神经编译可实现10-100倍速度提升，传统编译提速2-5倍。
- 支持自进化AI系统，适合快速迭代的高性能计算场景。
当前AI代码生成系统因编译、执行和测试阶段的CPU-GPU数据传输导致显著延迟。本文建立三种互补的GPU原生编译理论基础：(1) 适配GPU执行的并行传统编译；(2) 使用学习型序列到序列翻译与概率验证的神经编译；(3) 二者结合的混合架构。推导出延迟与能耗边界，表明代码迭代周期可实现10-100倍加速。分析显示，传统GPU编译通过消除传输可提升2-5倍，神经编译借助大规模并行实现10-100倍提速，混合方案则提供兼顾正确性与实用性的部署路径。本文形式化了概率验证框架，支持以编译精度换并行探索效率，并讨论其对自进化AI系统及未来类比计算底座的意义。
原文摘要 · Abstract (English)
Current AI code generation systems suffer from significant latency bottlenecks due to CPU-GPU data transfers during compilation, execution, and testing phases. We establish theoretical foundations for three complementary approaches to GPU-native compilation that eliminate these transfers: (1) parallel traditional compilation adapted for GPU execution, (2) neural compilation using learned sequence-to-sequence translation with probabilistic verification, and (3) hybrid architectures combining both strategies. We derive latency and energy bounds demonstrating potential speedups of 10-100x for code iteration cycles. Our analysis shows that traditional GPU compilation provides 2-5x improvements through transfer elimination, neural compilation achieves 10-100x speedups via massive parallelism, and hybrid approaches offer practical deployment paths with guaranteed correctness. We formalize the probabilistic verification framework that enables trading compilation accuracy for parallel exploration, and discuss implications for self-improving AI systems and future analog computing substrates.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。