MLComp: A Methodology for Machine Learning-based Performance Estimation
and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences
- URL: http://arxiv.org/abs/2012.05270v2
- Date: Fri, 11 Dec 2020 11:53:33 GMT
- Title: MLComp: A Methodology for Machine Learning-based Performance Estimation
and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences
- Authors: Alessio Colucci, D\'avid Juh\'asz, Martin Mosbeck, Alberto Marchisio,
Semeen Rehman, Manfred Kreutzer, Guenther Nadbath, Axel Jantsch and Muhammad
Shafique
- Abstract summary: We propose a novel Reinforcement Learning-based policy methodology for embedded software optimization.
We show that different Machine Learning models are automatically tested to choose the best-fitting one.
We also show that our framework can be trained efficiently for any target platform and application domain.
- Score: 10.200899224740871
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Embedded systems have proliferated in various consumer and industrial
applications with the evolution of Cyber-Physical Systems and the Internet of
Things. These systems are subjected to stringent constraints so that embedded
software must be optimized for multiple objectives simultaneously, namely
reduced energy consumption, execution time, and code size. Compilers offer
optimization phases to improve these metrics. However, proper selection and
ordering of them depends on multiple factors and typically requires expert
knowledge. State-of-the-art optimizers facilitate different platforms and
applications case by case, and they are limited by optimizing one metric at a
time, as well as requiring a time-consuming adaptation for different targets
through dynamic profiling.
To address these problems, we propose the novel MLComp methodology, in which
optimization phases are sequenced by a Reinforcement Learning-based policy.
Training of the policy is supported by Machine Learning-based analytical models
for quick performance estimation, thereby drastically reducing the time spent
for dynamic profiling. In our framework, different Machine Learning models are
automatically tested to choose the best-fitting one. The trained Performance
Estimator model is leveraged to efficiently devise Reinforcement Learning-based
multi-objective policies for creating quasi-optimal phase sequences.
Compared to state-of-the-art estimation models, our Performance Estimator
model achieves lower relative error (<2%) with up to 50x faster training time
over multiple platforms and application domains. Our Phase Selection Policy
improves execution time and energy consumption of a given code by up to 12% and
6%, respectively. The Performance Estimator and the Phase Selection Policy can
be trained efficiently for any target platform and application domain.
Related papers
- Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate [105.86576388991713]
We introduce a normalized gradient difference (NGDiff) algorithm, enabling us to have better control over the trade-off between the objectives.
We provide a theoretical analysis and empirically demonstrate the superior performance of NGDiff among state-of-the-art unlearning methods on the TOFU and MUSE datasets.
arXiv Detail & Related papers (2024-10-29T14:41:44Z) - Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems [3.2826250607043796]
Multi-Objective Reinforcement Learning (MORL) techniques exist but they have mostly been applied in RL benchmarks rather than real-world AS systems.
In this work, we use a MORL technique called Deep W-Learning (DWN) to find the optimal configuration for runtime performance optimization.
We compare DWN to two single-objective optimization implementations: epsilon-greedy algorithm and Deep Q-Networks.
arXiv Detail & Related papers (2024-08-02T11:16:09Z) - Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning [8.471466670802815]
We propose a multi-objective, multi-agent reinforcement learning (MARL) algorithm with high learning efficiency and low computational requirements.
We test our algorithm in an ITS environment with edge cloud computing.
Our algorithm also addresses various practical concerns with its modularized and asynchronous online training method.
arXiv Detail & Related papers (2024-03-13T18:05:16Z) - 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) - Leveraging Reinforcement Learning and Large Language Models for Code
Optimization [14.602997316032706]
This paper introduces a new framework to decrease the complexity of code optimization.
The proposed framework builds on large language models (LLMs) and reinforcement learning (RL)
We run several experiments on the PIE dataset using a CodeT5 language model and RRHF, a new reinforcement learning algorithm.
arXiv Detail & Related papers (2023-12-09T19:50:23Z) - CoRe Optimizer: An All-in-One Solution for Machine Learning [0.0]
Continuously resilient convergence (CoRe) shown superior performance compared to other state-of-the-art first-order gradient-based convergence algorithms.
CoRe yields best or competitive performance in every investigated application.
arXiv Detail & Related papers (2023-07-28T16:48:42Z) - 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) - VeLO: Training Versatile Learned Optimizers by Scaling Up [67.90237498659397]
We leverage the same scaling approach behind the success of deep learning to learn versatiles.
We train an ingest for deep learning which is itself a small neural network that ingests and outputs parameter updates.
We open source our learned, meta-training code, the associated train test data, and an extensive benchmark suite with baselines at velo-code.io.
arXiv Detail & Related papers (2022-11-17T18:39:07Z) - Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview [10.081056751778712]
We introduce the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML.
We provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization.
We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.
arXiv Detail & Related papers (2022-06-15T10:23:19Z) - Learning to Optimize: A Primer and A Benchmark [94.29436694770953]
Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods.
This article is poised to be the first comprehensive survey and benchmark of L2O for continuous optimization.
arXiv Detail & Related papers (2021-03-23T20:46:20Z) - Bayesian Optimization for Selecting Efficient Machine Learning Models [53.202224677485525]
We present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency.
Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency.
arXiv Detail & Related papers (2020-08-02T02:56:30Z)
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.