Portable models as a replacement for industrial heuristics in compiler optimizations

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

Recent advances in large language models (LLMs) have enabled automated kernel generation and optimization, but most existing approaches rely on surface signals such as compilation feedback and profiling metrics. These signals reveal that a kernel is slow, but not why the backend compiler fails to realize a profitable optimization, especially on emerging accelerators such as NPUs. We therefore formulate kernel optimization as a progressive cross-layer diagnosis problem that links runtime symptoms to IR structure and compiler behavior before rewriting source. Based on this insight, we present our system, a compiler-grounded and hierarchical optimization framework for Triton kernels. the system escalates from lightweight pattern triage and profiling diagnosis to IR attribution and compiler-grounded analysis only when deeper evidence is needed, then proposes evidence-backed source-level rewrites. We implement the system on Triton for Ascend NPUs and evaluate it on 37 successfully converted entries from a standardized NPUKernelBench-derived Ascend 950 benchmark. Across these entries, the system attains a geometric-mean speedup of 4.35$\times$ and a median speedup of 2.73$\times$ from the initial to optimized Triton kernel; 22/37 exceed 2$\times$ and 13/37 exceed 5$\times$. The complete distribution ranges from near-baseline entries to large wins, motivating transparent reporting of the current system's scope and limitations.

Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code

It is shown that generative compilation reduces non-compiling outputs and improves functional correctness, relative to standard post-generation feedback, by detecting a broad range of errors close to their source and early during generation, thereby reducing errors cascades and enabling focused diagnostics.

Cross-Architecture Autotuning for Single-Source Heterogeneous Programming Models

The rise of heterogeneous computing systems has intensified the need for performance-portable programming models and effective autotuning methodologies. Although compiler and runtime tuning are known to significantly influence application performance, it remains unclear how such optimizations transfer across different hardware architectures, particularly within single-source models such as SYCL. This work investigates the transferability of compile-time and runtime autotuning decisions across CPUs and GPUs, focusing on AdaptiveCpp, a SYCL implementation built on LLVM. We introduce an automated framework that jointly explores compiler flags and runtime parameters using both Bayesian optimization and a tabu-search–based strategy. The tool orchestrates compilation, execution, and measurement while also providing statistical attribution via ridge regression to quantify the impact of individual tuning parameters. Through an extensive evaluation of CPUs and GPUs from multiple vendors, we demonstrate that autotuning can deliver substantial performance gains—up to 3 × on CPUs—yet the influence of specific compiler flags often diverges across different architectures. For example, flags such as -fno-builtin yield large improvements on CPUs but have negligible effect on GPUs. We also demonstrate that runtime-level choices, such as thread-placement policies, can significantly affect performance on CPUs. Our findings highlight the challenges and opportunities of autotuning in heterogeneous, single-source programming ecosystems. They also underline the importance of architecture-aware autotuning strategies and motivate further exploration of cross-device performance modeling.

Effect of Abstractions and Prompting Strategies on LLM-Guided High-Performance Optimizations

It is demonstrated that LLMs provided with specific optimization goals achieve better measured performance and validity rates when generating C code compared to creating computation pipelines and optimization schedules with established frameworks, suggesting that future development should explore alternative approaches for verifiable LLM-guided code optimization.

An MLIR-Based Compilation Method for Large Language Models

An MLIR (Multi-Level Intermediate Representation) based compilation method for large language models, illustrated using two dialects of operators, TopOp and TpuOp, supporting a variety of generative models including the Qwen, Llama, InternVL, and MiniCPM-V series.

Characterizing Type Feedback in Just-In-Time Compilation