ORCID
- Tania Malik: 0000-0002-4461-7120
Abstract
WebAssembly (WASM) has emerged as a promising compilation target for languages traditionally not executed on the web and enable the cross-platform deployment of high-performance applications. While its general use cases have been well studied, the performance implications of executing Machine Learning (ML) workloads via WASM across different programming languages and runtime environments remain relatively unexplored. This paper presents a systematic evaluation of two representative ML models, K-Means and Logistic Regression, implemented in Python, Rust, and C++ and compiled to WASM. These models are executed in two distinct environments: a web browser and the WebAssembly System Interface (WASI), and their execution time and accuracy is compared against each programming language across both environments. This study aims to provide insight into the trade-offs and practical considerations involved in deploying ML workloads using WebAssembly across different language ecosystems and runtime configurations.
DOI Link
Publication Date
2025-09-09
Event
34th International Symposium on High-Performance Parallel and Distributed Computing
Publication Title
HPDC 2025 - Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing
Publisher
Association for Computing Machinery (ACM)
ISBN
9798400718694
Deposit Date
2026-07-28
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Khan, Sallar; Malik, Tania; and Hasanov, Khalid, "Performance Evaluation of Machine Learning Applications Using WebAssembly Across Different Programming Languages" (2025). Research Outputs: 2025-Present. 5.
https://arrow.tudublin.ie/dfrhro/5