What is it about?
This paper studies how well machine learning models run when compiled to WebAssembly (Wasm). We pick two models, K-Means and Logistic Regression, and compare their performance when written in Python, Rust, and other languages. We measure speed, resource use, etc
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Why is it important?
With more apps running in browsers or edge environments, WebAssembly is a key tool to bring fast, safe code to diverse platforms. But little is known about how ML workloads actually perform in Wasm. Our work fills that gap and guides developers on which languages and approaches make ML run well in those settings.
Perspectives
From this work, we see opportunities to optimize compilers, runtime systems, or ML libraries for Wasm. In future, one can explore more complex models (deep learning, decision trees), evaluate other languages, or tailor Wasm runtimes for resource-constrained environments. This opens the path for high-performance ML on web and edge
Sallar Khan
Technological University Dublin
Read the Original
This page is a summary of: Performance Evaluation of Machine Learning Applications Using WebAssembly Across Different Programming Languages, July 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3731545.3736817.
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