Model-Driven Rapid Prototyping for Control Algorithms with the GIPS Framework (System Description)

• URL: http://arxiv.org/abs/2503.20471v1
• Date: Wed, 26 Mar 2025 11:52:52 GMT
• Title: Model-Driven Rapid Prototyping for Control Algorithms with the GIPS Framework (System Description)
• Authors: Maximilian Kratz, Sebastian Ehmes, Philipp Maximilian Menzel, Andy Schürr,
• Abstract summary: We have created the GIPS (Graph-Based ILP Problem Specification) framework to support rapid prototyping of software systems.<n>Developers can use our high-level specification language GIPSL (Graph-Based ILP Problem Specification Language) to specify their desired model optimization as sets of constraints and objectives.<n>GIPS is able to derive executable (Java) software artifacts automatically that optimize a given input graph instance at runtime.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software engineers are faced with the challenge of creating control algorithms for increasingly complex dynamic systems, such as the management of communication network topologies. To support rapid prototyping for these increasingly complex software systems, we have created the GIPS (Graph-Based ILP Problem Specification) framework to derive some or even all of the building blocks of said systems, by using Model-Driven Software Engineering (MDSE) approaches. Developers can use our high-level specification language GIPSL (Graph-Based ILP Problem Specification Language) to specify their desired model optimization as sets of constraints and objectives. GIPS is able to derive executable (Java) software artifacts automatically that optimize a given input graph instance at runtime, according to the specification. Said artifacts can then be used as system blocks of, e.g., topology control systems. In this paper, we present the maintenance of (centralized) tree-based peer-to-peer data distribution topologies as a possible application scenario for GIPS in the topology control domain. The presented example is implemented using open-source software and its source code as well as an executable demonstrator in the form of a virtual machine is available on GitHub.

Related papers

• Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs [63.10710876536337]
  We propose an offline simulation framework to curate a software-specific skillset, a collection of verified scripts. Our framework comprises two components: (1) task creation, using top-down functionality and bottom-up API synergy exploration to generate helpful tasks. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs.
  arXiv  Detail & Related papers  (2025-04-29T04:03:37Z)
• Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo [90.78001821963008]
  A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. We develop an architecture for controlled LM generation based on sequential Monte Carlo (SMC) Our system builds on the framework of Lew et al. (2023) and integrates with its language model probabilistic programming language.
  arXiv  Detail & Related papers  (2025-04-17T17:49:40Z)
• AlgOS: Algorithm Operating System [2.5352713493505785]
  AlgOS is an unopinionated, modular framework for algorithmic implementations. It is designed to reduce the overhead of implementing new algorithms and to standardise the comparison of algorithms.
  arXiv  Detail & Related papers  (2025-04-07T10:36:46Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph [63.87660059104077]
  We present RepoGraph, a plug-in module that manages a repository-level structure for modern AI software engineering solutions.<n>RepoGraph substantially boosts the performance of all systems, leading to a new state-of-the-art among open-source frameworks.
  arXiv  Detail & Related papers  (2024-10-03T05:45:26Z)
• Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow [3.2821049498759094]
  We propose a novel model- and feature-based approach to development of vehicle software systems. One of the key points of the presented approach is the inclusion of modern generative AI, specifically Large Language Models (LLMs) The resulting pipeline is automated to a large extent, with feedback being generated at each step.
  arXiv  Detail & Related papers  (2024-03-21T15:07:57Z)
• Distributed Inference and Fine-tuning of Large Language Models Over The Internet [91.00270820533272]
  Large language models (LLMs) are useful in many NLP tasks and become more capable with size. These models require high-end hardware, making them inaccessible to most researchers. We develop fault-tolerant inference algorithms and load-balancing protocols that automatically assign devices to maximize the total system throughput.
  arXiv  Detail & Related papers  (2023-12-13T18:52:49Z)
• ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation [38.023395256208055]
  High-level synthesis (HLS) allows a developer to compile a high-level description in the form of software code in C and C++. HLS tools still require microarchitecture decisions, expressed in terms of pragmas. We propose ProgSG allowing the source code sequence modality and the graph modalities to interact with each other in a deep and fine-grained way.
  arXiv  Detail & Related papers  (2023-05-18T09:44:18Z)
• ParaGraph: Weighted Graph Representation for Performance Optimization of HPC Kernels [1.304892050913381]
  We introduce a new graph-based program representation for parallel applications that extends the Abstract Syntax Tree. We evaluate our proposed representation by training a Graph Neural Network (GNN) to predict the runtime of an OpenMP code region. Results show that our approach is indeed effective and has normalized RMSE as low as 0.004 to at most 0.01 in its runtime predictions.
  arXiv  Detail & Related papers  (2023-04-07T05:52:59Z)
• End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning [13.810753108848582]
  We propose a unified, end-to-end, programmable graph representation learning framework. It is capable of mining the complexity of high-level programs down to the universal intermediate representation, extracting the specific computational patterns and predicting which code segments would run best on a specific core. In the evaluation, we demonstrate a maximum speedup of 6.42x compared to the thread-based execution, and 2.02x compared to the state-of-the-art technique.
  arXiv  Detail & Related papers  (2022-04-25T22:13:13Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Synthetic Datasets for Neural Program Synthesis [66.20924952964117]
  We propose a new methodology for controlling and evaluating the bias of synthetic data distributions over both programs and specifications. We demonstrate, using the Karel DSL and a small Calculator DSL, that training deep networks on these distributions leads to improved cross-distribution generalization performance.
  arXiv  Detail & Related papers  (2019-12-27T21:28:10Z)

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.