PerfDojo: Automated ML Library Generation for Heterogeneous Architectures

• URL: http://arxiv.org/abs/2511.03586v1
• Date: Wed, 05 Nov 2025 16:05:26 GMT
• Title: PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
• Authors: Andrei Ivanov, Siyuan Shen, Gioele Gottardo, Marcin Chrapek, Afif Boudaoud, Timo Schneider, Luca Benini, Torsten Hoefler,
• Abstract summary: We introduce PerfLLM, a novel automatic optimization methodology leveraging Large Language Models (LLMs) and Reinforcement Learning (RL)<n>Central to this is PerfDojo, an environment framing optimization as an RL game using a human-readable, mathematically-inspired code representation that guarantees semantic validity through transformations.<n>We demonstrate PerfLLM's ability to achieve significant performance gains across diverse CPU (x86, Arm, RISC-V) and GPU architectures.
• Score: 28.513777562827485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a significant challenge. Heterogeneity in instruction sets, specialized kernel requirements for different data types and model features (e.g., sparsity, quantization), and architecture-specific optimizations complicate performance tuning. Manual optimization is resource-intensive, while existing automatic approaches often rely on complex hardware-specific heuristics and uninterpretable intermediate representations, hindering performance portability. We introduce PerfLLM, a novel automatic optimization methodology leveraging Large Language Models (LLMs) and Reinforcement Learning (RL). Central to this is PerfDojo, an environment framing optimization as an RL game using a human-readable, mathematically-inspired code representation that guarantees semantic validity through transformations. This allows effective optimization without prior hardware knowledge, facilitating both human analysis and RL agent training. We demonstrate PerfLLM's ability to achieve significant performance gains across diverse CPU (x86, Arm, RISC-V) and GPU architectures.

Related papers

• PEAK: A Performance Engineering AI-Assistant for GPU Kernels Powered by Natural Language Transformations [0.8699280339422538]
  We introduce PEAK, a Performance Engineering AI-Assistant for Kernels powered by natural language transformations.<n>We show that our implementations are competitive with vendor libraries when available, and for HLSL (without a library) our documented FLOPS.
  arXiv  Detail & Related papers  (2025-12-22T04:15:24Z)
• xLLM Technical Report [57.13120905321185]
  We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework.<n>xLLM builds a novel decoupled service-engine architecture.<n>xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources.
  arXiv  Detail & Related papers  (2025-10-16T13:53:47Z)
• REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving [6.19179006129561]
  We introduce a novel compilation framework (dubbed Reasoning) that formulates optimization as a sequential, context-aware decision process.<n>Our approach demonstrates the potential of LLM-guided reasoning to transform the landscape of compiler optimization.
  arXiv  Detail & Related papers  (2025-06-02T07:02:46Z)
• Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling [3.253908111652627]
  Large Language Models (LLMs) often struggle to generate formally correct and usable models against hallucinations.<n>We present a novel framework that significantly improves the authenticity of LLMs for optimization modeling using Reinforcement Learning with Verifiable Reward.
  arXiv  Detail & Related papers  (2025-05-17T02:32:03Z)
• AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs [68.99086112477565]
  Transformer-based large language models (LLMs) have demonstrated exceptional capabilities in sequence modeling and text generation.<n>Existing heterogeneous training methods significantly expand the scale of trainable models but introduce substantial communication overheads and CPU workloads.<n>We propose AutoHete, an automatic and efficient heterogeneous training system compatible with both single- GPU and multi- GPU environments.
  arXiv  Detail & Related papers  (2025-02-27T14:46:22Z)
• Inference Optimization of Foundation Models on AI Accelerators [68.24450520773688]
  Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI. As the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. This tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators.
  arXiv  Detail & Related papers  (2024-07-12T09:24:34Z)
• Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics [10.22111332588471]
  We propose a novel deep symbolic optimization learning framework that combines their advantages. Dso4NS guides the search for mathematical expressions within the high-dimensional discrete symbolic space and then incorporates the highest-performing mathematical expressions into a solver. Experiments demonstrate the effectiveness of Dso4NS in learning high-quality expressions, outperforming existing approaches on a CPU machine.
  arXiv  Detail & Related papers  (2024-06-14T06:02:14Z)
• CompilerDream: Learning a Compiler World Model for General Code Optimization [58.87557583347996]
  We introduce CompilerDream, a model-based reinforcement learning approach to general code optimization.<n>It comprises a compiler world model that accurately simulates the intrinsic properties of optimization passes and an agent trained on this model to produce effective optimization strategies.<n>It excels across diverse datasets, surpassing LLVM's built-in optimizations and other state-of-the-art methods in both settings of value prediction and end-to-end code optimization.
  arXiv  Detail & Related papers  (2024-04-24T09:20:33Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Learning Performance-Improving Code Edits [107.21538852090208]
  We introduce a framework for adapting large language models (LLMs) to high-level program optimization. First, we curate a dataset of performance-improving edits made by human programmers of over 77,000 competitive C++ programming submission pairs. For prompting, we propose retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.
  arXiv  Detail & Related papers  (2023-02-15T18:59:21Z)
• A Learned Performance Model for Tensor Processing Units [5.733911161090224]
  We demonstrate a method of learning performance models from a corpus of graph programs for Processing Unit (TPU) instances. We show that our learned model outperforms a heavily-optimized analytical performance model on two tasks. It helps an autotuner discover faster programs in a setting where access to TPUs is limited or expensive.
  arXiv  Detail & Related papers  (2020-08-03T17:24:52Z)
• PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives [55.79741270235602]
  We present compiler algorithms to automatically generate high performance implementations of Deep Learning primitives. We develop novel data reuse analysis algorithms using the polyhedral model. We also show that such a hybrid compiler plus a minimal library-use approach results in state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-06-02T06:44:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.