Speakers
Description
JavaScript remains the dominant language for
client-side scripting, while WebAssembly offers near-native ex
ecution speeds, making it a compelling choice for computa
tionally intensive tasks. This study provides a comprehensive
analysis of the performance differences between WebAssembly
and JavaScript across various computing environments, includ
ing different browsers (Firefox, Chrome, Edge) and platforms
(desktop and mobile) [1], [2].
To evaluate computational efficiency, we conducted a series
of benchmark tests, including integer operations (Sieve of Er
atosthenes, sorting algorithms) [3], floating-point calculations
(numerical integration, Monte Carlo method) [4], and recursive
computations (Fibonacci sequence, matrix multiplication) [5].
Additionally, we investigated the impact of WebAssembly on ma
chine learning workloads by utilizing minimalist implementations
such as TinyDNN for digit classification on the MNIST dataset
[6]. Our findings indicate that WebAssembly consistently outper
forms JavaScript in CPU-bound tasks, particularly in integer
operations and recursive computations. However, JavaScript’s
just-in-time (JIT) compilation allows it to remain competitive
in some floating-point calculations.
One focus of our study was the application of WebAssembly in
browser-based machine learning. We examined the performance
of lightweight neural network implementations, emphasizing
WebAssembly’s ability to accelerate tensor computations directly
in the browser. This capability is crucial for deploying AI models
on the client side, reducing reliance on cloud-based services,
improving privacy, and minimizing latency. Our analysis also
explores WebAssembly’s potential for real-time applications such
as image classification, object detection, and natural language
processing.
Beyond raw performance metrics, this study assesses the
broader implications of WebAssembly’s adoption in web develop
ment. One of its key advantages is its ability to support multiple
programming languages, including Rust, C, and C++, allowing
developers to leverage high-performance libraries within the
browser environment [7]. Additionally, WebAssembly’s sandbox
ing mechanisms enhance security by isolating execution, reducing
potential attack vectors compared to traditional JavaScript-based
applications.
Despite its advantages, WebAssembly has limitations. Exe
cution performance varies across browsers, and its integration
with JavaScript-based applications presents challenges due to
data serialization overhead. Moreover, WebAssembly lacks direct
access to the DOM, necessitating JavaScript as an intermediary
for UI interactions.
As WebAssembly continues to evolve, its role in performance
critical web applications is expected to expand, particularly
in fields such as cryptography, data processing, and real-time
machine learning.
Keywords—WebAssembly, JavaScript, Performance Compar
ison, Machine Learning, Convolutional Neural Networks.
