Cross-Architecture Autotuning for Single-Source Heterogeneous Programming Models

Paper in proceeding, 2026

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.