mlsanityData to 7 Oct 2026

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

arXiv 2212.11142 ↗First posted 1 Dec 2022cs.PL cs.LG cs.PFcited by 11

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Abstract (arXiv)

We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, it deals with permutation, ordered, and continuous parameter types along with both known and unknown parameter constraints. To reason about these parameter types and efficiently deliver high-quality code, BaCO uses Bayesian optimiza tion algorithms specialized towards the autotuning domain. We demonstrate BaCO's effectiveness on three modern compiler systems: TACO, RISE & ELEVATE, and HPVM2FPGA for CPUs, GPUs, and FPGAs respectively. For these domains, BaCO outperforms current state-of-the-art autotuners by delivering on average 1.36x-1.56x faster code with a tiny search budget, and BaCO is able to reach expert-level performance 2.9x-3.9x faster.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 22 of its 64 references that are in the map. First posted on arXiv 1 Dec 2022.

Builds on

22 of 64 references.

References · 22

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
  3. 2020Sparse GPU Kernels for Deep Learningcited by 138
  4. 2016Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasetscited by 131
  5. 2017Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processescited by 65
  6. 2018Practical Design Space Explorationcited by 35
  7. 2021IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learningcited by 26
  8. 2019BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameterscited by 25
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  11. 2018Format Abstraction for Sparse Tensor Algebra Compilerscited by 16
  12. 2020Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimizationcited by 13
10 more
  1. 2018Antithetic and Monte Carlo kernel estimators for partial rankingscited by 10
  2. 2020ProTuner: Tuning Programs with Monte Carlo Tree Searchcited by 9
  3. 2019Multitask and Transfer Learning for Autotuning Exascale Applicationscited by 7
  4. 2019A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkitcited by 6
  5. 2020Autotuning Search Space for Loop Transformationscited by 6
  6. 2021A comparison of mixed-variables Bayesian optimization approachescited by 6
  7. 2021Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformationscited by 4
  8. 2022HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimizationcited by 4
  9. 2022RISE & Shine: Language-Oriented Compiler Designcited by 2
  10. 2021A Graph Deep Learning Framework for High-Level Synthesis Design Space Explorationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 5; 2026: 2.

11 papers.

  1. 2023Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spacescited by 27
  2. 2023Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddingscited by 24
  3. 2025STARK: Strategic Team of Agents for Refining Kernelscited by 23
  4. 2024SCOOT: SLO-Oriented Performance Tuning for LLM Inference Enginescited by 9
  5. 2025REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Servingcited by 3
  6. 2026Kareus: Joint Reduction of Dynamic and Static Energy in Large Model Trainingcited by 3
  7. 2024CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimizationcited by 2
  8. 2025CoTune: Co-evolutionary Configuration Tuningcited by 2
  9. 2025Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuningcited by 1
  10. 2025Automated Algorithm Design for Auto-Tuning Optimizerscited by 0
  11. 2026Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Rankingcited by 0