# Kernel Launcher: C++ Library for Optimal-Performance Portable CUDA Applications

arXiv: https://arxiv.org/abs/2303.12374 · HTML: https://mlsanity.com/paper/2303.12374/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [STARK: Strategic Team of Agents for Refining Kernels](https://mlsanity.com/paper/2510.16996/) (2025)
- [Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning](https://mlsanity.com/paper/2509.26300/) (2025)
- [Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search](https://mlsanity.com/paper/2604.11109/) (2026)

## Abstract (arXiv)

Graphic Processing Units (GPUs) have become ubiquitous in scientific computing. However, writing efficient GPU kernels can be challenging due to the need for careful code tuning. To automatically explore the kernel optimization space, several auto-tuning tools - like Kernel Tuner - have been proposed. Unfortunately, these existing auto-tuning tools often do not concern themselves with integration of tuning results back into applications, which puts a significant implementation and maintenance burden on application developers. In this work, we present Kernel Launcher: an easy-to-use C++ library that simplifies the creation of highly-tuned CUDA applications. With Kernel Launcher, programmers can capture kernel launches, tune the captured kernels for different setups, and integrate the tuning results back into applications using runtime compilation. To showcase the applicability of Kernel Launcher, we consider a real-world computational fluid dynamics code and tune its kernels for different GPUs, input domains, and precisions.