WebAssembly & ML Performance
Performance Evaluation of Machine Learning Applications Using WebAssembly Across Different Programming Languages
This paper presents a systematic evaluation of Machine Learning (ML) workloads using WebAssembly (WASM) across different programming languages (Python, Rust, C++) and runtime environments (web browser, WASI). Rust emerged as the most performant and consistent, while Python showed higher variability and overhead due to its interpreted nature and library dependencies. The study highlights the trade-offs in deploying ML workloads via WebAssembly.
Key Executive Impact Metrics
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Performance improvement with Rust WASM vs Python Browser
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Accuracy maintained across C++/Rust WASM deployments
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Python browser LR for 100K records
Deep Analysis & Enterprise Applications
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WebAssembly (WASM) is evolving beyond browser-centric applications, providing a portable, low-level binary format for high-performance execution. This study investigates its suitability for Machine Learning (ML) workloads, comparing Python, Rust, and C++ implementations across browser and WebAssembly System Interface (WASI) environments.
ML Model WASM Deployment Process
Choose ML Model (K-Means/Logistic Regression)
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Implement in Python, Rust, or C++
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Compile to WASM using relevant toolchain
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Execute in Browser or WASI Runtime
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Measure Performance & Accuracy
0.7s
Rust execution time for largest dataset (100K records) in WASI
64s
Python browser execution time for largest dataset (100K records) for LR
| Language | Execution Time (s) | Accuracy (%) | Key Advantages |
|---|---|---|---|
| Rust | <1s | 93-96% |
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| C++ | 8-38s | 93-96% |
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| Python | >60s | 49-92% |
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Impact of WASI on Python ML
A significant challenge for Python's Logistic Regression model in WASI was its 49% accuracy compared to 96% in the browser. This gap is attributed to WASI's current lack of support for dynamic linking required by NumPy's compiled C extensions. This highlights critical dependencies when moving Python ML to WASI.
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Your WASM ML Implementation Roadmap
A structured approach to integrate WebAssembly-powered ML into your enterprise architecture for optimal performance.
Phase 1: Discovery & Strategy
Understand current ML deployments, identify suitable models, and define WASM integration strategy.
Phase 2: PoC Development
Implement K-Means/Logistic Regression in Rust/C++/Python, compile to WASM, and establish baseline performance.
Phase 3: Performance Optimization
Iterate on language choices, optimize WASM builds, and tune runtime configurations for target environments.
Phase 4: Integration & Deployment
Seamlessly integrate WASM ML modules into existing web or serverless infrastructure.
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