The Need for Speed of AI Applications: Performance Comparison of Native vs. Browser-based Algorithm Implementations
This addresses performance bottlenecks for developers and users of client-side AI applications, such as federated learning, by evaluating computational efficiency in browser environments.
The paper compared the performance of native code versus browser-based implementations (JavaScript, ASM.js, WebAssembly) for AI algorithms, finding that optimized runtimes can achieve or exceed native binary performance.
AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence of client-side (federated) machine learning algorithms, driven by the interests of large corporations and startups alike. Apart from mathematical and algorithmic concerns, this trend especially demands new levels of computational efficiency from client environments. Consequently, this paper deals with the question of state-of-the-art performance by presenting a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms. Our results show that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. We analyze the results obtained and speculate on the reasons behind some surprises, rounding the paper off by outlining future possibilities as well as some of our own research efforts.