ESwML 2024

Empowering Software through Machine Learning (ESwML)

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Empowering Software through Machine Learning (ESwML)


The software of tomorrow will heavily rely on the use of machine learning models. This will span various aspects including using Machine Learning (ML) models during the development time to enhance developer productivity, designing ML heuristics to improve application execution, and adopting surrogate Neural Networks (NN) models within applications to replace expensive computations and accelerate their performance. However, several challenges limit the broad adoption of ML in today’s software.

For example, there are no programming language extensions that can capture the developer’s intent to use surrogate NN models in their applications, nor can task scheduling algorithms communicate seamlessly with ML heuristics to decide and schedule tasks. As applications continue to get integrated into complex, deep software stacks with workflows, compilers, runtime libraries, and heterogeneous systems, it becomes necessary to use novel techniques for assisting software development, supporting the application execution orchestration, and potentially improving application performance.

The goal of Empowering Software through Machine Learning (ESwML) workshop is to establish a platform where researchers, scientists, application developers, computing center staff, and industry professionals can come together to exchange ideas and explore how artificial intelligence can help in effective and efficient use of future systems.

This workshop will actively drive discussion and aim to answer the following questions:



April 22nd, 2024

Session 1

13:45 - 14:30 (incl. 10 min Q&A)

Is Machine Learning Necessary to Use in Cloud Resource Management?
Thaleia Dimitra Doudali, IMDEA Software Institute, Madrid, Spain

14:30 - 15:15 (incl. 10 min Q&A)

Towards Transparency in Computational Footprint of Deep Learning
Pinar Tözün, IT University of Copenhagen, Denmark

Coffee Break (15:15 - 15:45)

Session 2

15:45 - 16:30 (incl. 10 min Q&A)

Challenges and Automation When Using Machine Learning Surrogates in Scientific Applications
Konstantinos Parasyris, Lawrence Livermore National Laboratory, USA

16:30 - 17:15 (incl. 10 min Q&A)

Auto-HPCnet: an Automatic Framework to Build Neural Network-based Surrogate for HPC Applications
Dong Li, University of California, Merced, CA USA


Workshop Co-chairs


Attendance at this workshop is part of the registration for Eurosys 2024. See here to register.

Topics of Interest

Topics of interest to the ML4SW workshop include but are not limited to:

Important Deadlines

Submission due date: February 15, 2024 (AoE)

Author notification: March 8, 2024

Camera-ready papers: March 13, 2024


Full papers may not exceed 8 single-spaced double-column pages.

Papers must be submitted through HotCRP: