# ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

arXiv: https://arxiv.org/abs/2303.16245 · HTML: https://mlsanity.com/paper/2303.16245/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 81 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [A Survey on Compiler Autotuning using Machine Learning](https://mlsanity.com/paper/1801.04405/) (2018)
- [CLTune: A Generic Auto-Tuner for OpenCL Kernels](https://mlsanity.com/paper/1703.06503/) (2017)
- [BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters](https://mlsanity.com/paper/1908.06756/) (2019)
- [Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization](https://mlsanity.com/paper/2010.08040/) (2020)
- [ProTuner: Tuning Programs with Monte Carlo Tree Search](https://mlsanity.com/paper/2005.13685/) (2020)
- [Autotuning Search Space for Loop Transformations](https://mlsanity.com/paper/2010.06521/) (2020)
- [Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations](https://mlsanity.com/paper/2105.04555/) (2021)
- [Towards Autotuning of OpenMP Applications on Multicore Architectures](https://mlsanity.com/paper/1401.4063/) (2014)
- [User-Directed Loop-Transformations in Clang](https://mlsanity.com/paper/1811.00624/) (2018)
- [On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU](https://mlsanity.com/paper/1904.03383/) (2019)
- [libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations](https://mlsanity.com/paper/2104.08322/) (2021)
- [Toward an End-to-End Auto-tuning Framework in HPC PowerStack](https://mlsanity.com/paper/2008.06571/) (2020)

## Built on by (most cited first, 13 of 13)

- [Leveraging LLMs to Automate Energy-Aware Refactoring of Parallel Scientific Codes](https://mlsanity.com/paper/2505.02184/) (2025)
- [Autotuning Apache TVM-based Scientific Applications Using Bayesian Optimization](https://mlsanity.com/paper/2309.07235/) (2023)
- [Transfer-Learning-Based Autotuning Using Gaussian Copula](https://mlsanity.com/paper/2401.04669/) (2024)
- [CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization](https://mlsanity.com/paper/2406.17811/) (2024)
- [MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations](https://mlsanity.com/paper/2407.02238/) (2024)
- [Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets](https://mlsanity.com/paper/2507.00298/) (2025)
- [Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning](https://mlsanity.com/paper/2509.26300/) (2025)
- [Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality](https://mlsanity.com/paper/2403.08131/) (2024)
- [An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors](https://mlsanity.com/paper/2406.18445/) (2024)
- [Generalizing Scaling Laws for Dense and Sparse Large Language Models](https://mlsanity.com/paper/2508.06617/) (2025)
- [Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling](https://mlsanity.com/paper/2509.11000/) (2025)
- [Automated Algorithm Design for Auto-Tuning Optimizers](https://mlsanity.com/paper/2510.17899/) (2025)
- [Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking](https://mlsanity.com/paper/2609.15807/) (2026)

## Abstract (arXiv)

As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low-overhead autotuning framework to autotune performance and energy for various hybrid MPI/OpenMP scientific applications at large scales and to explore the tradeoffs between application runtime and power/energy for energy efficient application execution, then use this framework to autotune four ECP proxy applications -- XSBench, AMG, SWFFT, and SW4lite. Our approach uses Bayesian optimization with a Random Forest surrogate model to effectively search parameter spaces with up to 6 million different configurations on two large-scale production systems, Theta at Argonne National Laboratory and Summit at Oak Ridge National Laboratory. The experimental results show that our autotuning framework at large scales has low overhead and achieves good scalability. Using the proposed autotuning framework to identify the best configurations, we achieve up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP improvement on up to 4,096 nodes.