Theoretical Foundations of GPU-Native Compilation for Rapid Code Iteration

2025-12-11

AI Summary

CPU-GPU data transfers in AI-based code generation impose severe compilation and iterative latency bottlenecks. Method: This paper introduces the first theoretical framework for GPU-native compilation, systematically proposing and analyzing three paradigms—parallel traditional compilation, neural compilation, and hybrid compilation. Contributions/Results: (1) A probabilistic formal verification mechanism that jointly optimizes accuracy and parallelism; (2) Theoretical characterization of latency, energy, and correctness trade-offs across all three paradigms, along with provable co-optimization pathways; (3) A deployable hybrid architecture ensuring strict correctness. Experimental and theoretical analysis demonstrates up to 10–100× speedup in end-to-end code iteration latency: traditional GPU compilation accelerates by 2–5×, neural compilation by 10–100×, and the hybrid approach achieves practical efficiency without compromising formal correctness.

Technology Category

Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Hardware-aware MLCognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Eliminates CPU-GPU data transfer bottlenecks in AI code compilation.
• Establishes GPU-native compilation methods for faster code iteration cycles.
• Formalizes probabilistic verification to trade accuracy for parallel exploration.

Innovation

• GPU-native compilation eliminates CPU-GPU data transfers
• Neural compilation uses sequence-to-sequence translation with verification
• Hybrid approaches combine traditional and neural compilation strategies