Discovering Software Parallelization Points Using Deep Neural Networks

• URL: http://arxiv.org/abs/2509.16215v2
• Date: Wed, 01 Oct 2025 23:33:46 GMT
• Title: Discovering Software Parallelization Points Using Deep Neural Networks
• Authors: Izavan dos S. Correia, Henrique C. T. Santos, Tiago A. E. Ferreira,
• Abstract summary: This study proposes a deep learning-based approach for discovering loops in programming code according to their potential for parallelization.<n>Two genetic algorithm-based code generators were developed to produce two distinct types of code: independent loops, which are parallelizable, and ambiguous loops, whose dependencies are unclear.<n>Two deep learning models - a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN) - were implemented to perform the classification.
• Score: 1.5866931449827322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study proposes a deep learning-based approach for discovering loops in programming code according to their potential for parallelization. Two genetic algorithm-based code generators were developed to produce two distinct types of code: (i) independent loops, which are parallelizable, and (ii) ambiguous loops, whose dependencies are unclear, making them impossible to define if the loop is parallelizable or not. The generated code snippets were tokenized and preprocessed to ensure a robust dataset. Two deep learning models - a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN) - were implemented to perform the classification. Based on 30 independent runs, a robust statistical analysis was employed to verify the expected performance of both models, DNN and CNN. The CNN showed a slightly higher mean performance, but the two models had a similar variability. Experiments with varying dataset sizes highlighted the importance of data diversity for model performance. These results demonstrate the feasibility of using deep learning to automate the identification of parallelizable structures in code, offering a promising tool for software optimization and performance improvement.

Related papers

• Parallel Neural Networks in Golang [0.0]
  This paper describes the design and implementation of parallel neural networks (PNNs) with the novel programming language Golang. Golang and its inherent parallelization support proved very well for parallel neural network simulation by considerable decreased processing times compared to sequential variants.
  arXiv  Detail & Related papers  (2023-04-19T11:56:36Z)
• SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation [65.4396959244269]
  The paper tackles the challenge by designing a general framework to construct 3D learning architectures. The proposed approach can be applied to general backbones like PointNet and DGCNN. Experiments on ModelNet40, ShapeNet, and the real-world dataset ScanObjectNN, demonstrated that the method achieves a great trade-off between efficiency, rotation, and accuracy.
  arXiv  Detail & Related papers  (2022-09-13T12:12:19Z)
• Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks [0.07614628596146598]
  We study deep neural networks with binary activation functions and continuous or integer weights (BDNN) We show that the BDNN can be reformulated as a mixed-integer linear program with bounded weight space which can be solved to global optimality by classical mixed-integer programming solvers. For the first time a robust model is presented which enforces robustness of the BDNN during training.
  arXiv  Detail & Related papers  (2021-10-21T18:02:58Z)
• Optimising for Interpretability: Convolutional Dynamic Alignment Networks [108.83345790813445]
  We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets) Their core building blocks are Dynamic Alignment Units (DAUs), which are optimised to transform their inputs with dynamically computed weight vectors that align with task-relevant patterns. CoDA Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions.
  arXiv  Detail & Related papers  (2021-09-27T12:39:46Z)
• Parareal Neural Networks Emulating a Parallel-in-time Algorithm [1.988145627448243]
  As deep neural networks (DNNs) become deeper, the training time increases. In this paper, we introduce a novel methodology to construct a parallel neural network.
  arXiv  Detail & Related papers  (2021-03-16T02:03:39Z)
• Solving Mixed Integer Programs Using Neural Networks [57.683491412480635]
  This paper applies learning to the two key sub-tasks of a MIP solver, generating a high-quality joint variable assignment, and bounding the gap in objective value between that assignment and an optimal one. Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP. We evaluate our approach on six diverse real-world datasets, including two Google production datasets and MIPLIB, by training separate neural networks on each.
  arXiv  Detail & Related papers  (2020-12-23T09:33:11Z)
• AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data [11.552769149674544]
  Development of high-performing predictive models for large data sets is a challenging task. Recent automated machine learning (AutoML) is emerging as a promising approach to automate predictive model development. We have developed AgEBO-Tabular, an approach to combine aging evolution (AgE) and a parallel NAS method that searches over neural architecture space.
  arXiv  Detail & Related papers  (2020-10-30T16:28:48Z)
• Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning [58.14930566993063]
  We present connections between three models used in different research fields: weighted finite automata(WFA) from formal languages and linguistics, recurrent neural networks used in machine learning, and tensor networks. We introduce the first provable learning algorithm for linear 2-RNN defined over sequences of continuous vectors input.
  arXiv  Detail & Related papers  (2020-10-19T15:28:00Z)
• Binarizing MobileNet via Evolution-based Searching [66.94247681870125]
  We propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet. Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs) Our objective is to come up with a tiny yet efficient binary neural architecture by exploring the best candidates of the group convolution.
  arXiv  Detail & Related papers  (2020-05-13T13:25:51Z)
• Adaptive Explainable Neural Networks (AxNNs) [8.949704905866888]
  We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability. For predictive performance, we build a structured neural network made up of ensembles of generalized additive model networks and additive index models. For interpretability, we show how to decompose the results of AxNN into main effects and higher-order interaction effects.
  arXiv  Detail & Related papers  (2020-04-05T23:40:57Z)
• Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving [106.63673243937492]
  Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning. We frame the task of feedforward computation as solving a system of nonlinear equations. We then propose to find the solution using a Jacobi or Gauss-Seidel fixed-point method, as well as hybrid methods of both. Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced (or equal) number of parallelizable iterations, and hence reduced time given sufficient parallel computing power.
  arXiv  Detail & Related papers  (2020-02-10T10:11:31Z)

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.