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The Future of Numerical Computing in JavaScript

JavaScript is evolving beyond web development and into the realm of high-performance numerical computing. In this talk, we’ll explore the current landscape of scientific computing in JavaScript, compare its performance with Python and C, and discuss what’s missing. Through live demos – including AI, real-time data processing, and mathematical computing – we’ll see how libraries like stdlib and a few others are shaping the future.

This talk has been presented at JSNation 2025, check out the latest edition of this JavaScript Conference.

FAQ

Performing numerical computing tasks inside a web browser offers instant access without needing to install additional software, ensures privacy and security by keeping data on the client's machine, provides low latency by eliminating server processing delays, and increases accessibility for users without high-end machines.

TensorFlow.js allows for machine learning tasks to be performed entirely in the browser, from data pre-processing to inference, without involving any server-side processing.

Tesseract.js is a popular tool used for OCR in JavaScript. It is a JavaScript port of the Tesseract OCR engine and can extract text from images entirely within the browser.

WebAssembly allows for performance-heavy applications to run at near-native speeds in web browsers by compiling code from languages like C or C++ into a binary format. It supports SIMD functions, enabling efficient computations.

JavaScript lacks native support for certain data structures like ndarrays and data frames, which are essential for scientific computing. It also faces challenges in providing consistent performance and ease of use compared to other languages like Python.

Python libraries like NumPy for numerical operations, Pandas for data manipulation, and scikit-learn for machine learning serve as models for JavaScript libraries that aim to provide similar capabilities in the web environment.

Vectorization allows operations to be applied to entire arrays rather than individual elements, significantly improving performance and efficiency, especially with large datasets.

Standard Lib generally outperforms Pyodide, especially for smaller datasets, due to its efficient use of ndarrays, universal functions, and optimized performance without the overhead of loading Python libraries.

WebGazer uses the webcam to estimate where a user is looking on the screen, enabling interactive applications that respond to eye movements directly in the browser.

Standard Lib provides a common foundation with standardized APIs for building domain-specific libraries, supports ndarrays, offers functions for linear algebra and statistical analysis, and facilitates fast numerical computations through vectorization.

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Gunj Joshi

21 min

16 Jun, 2025

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Video Summary and Transcription

Gunj Joshi explores the future of numerical computing in JavaScript, showcasing benefits like privacy, low latency, and accessibility. The talk delves into training models inside browsers, challenges with TensorFlow.js, and the importance of syntactical niceties for readability. It highlights the significance of Jupyter Notebooks, Observable for live coding, and the impact of WebAssembly on JavaScript's performance. The discussion emphasizes NumPy's superiority in numerical applications, the importance of vectorization, and the potential of JavaScript's ecosystem for scientific applications. Additionally, it covers the efficiency of vectorized data processing, performance differences in data processing approaches, and the high performance of WebAssembly in web browsers.

Available in Español: El Futuro de la Computación Numérica en JavaScript

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