Concurrent JavaScript Parsing for Faster Loading of Web Apps

Abstract

JavaScript is a dynamic language mainly used as a client-side web script. Nowadays, web is evolving into an application platform with its web apps, and JavaScript increasingly undertakes complex computations and interactive user interfaces, requiring a high-performance JavaScript engine. There have been many optimizations for efficient JavaScript engines, but one component that has not been optimized much is JavaScript parsing. A JavaScript function needs to be parsed before being executed, and the parsing overhead takes a substantial portion of JavaScript execution time for web apps, especially during app loading. This article proposes concurrent parsing of JavaScript, which performs the parsing of JavaScript functions in advance on different threads, while the main thread is executing the parsed JavaScript functions. This can hide the parsing overhead from the main execution thread, reducing the JavaScript execution time, thus reducing the overall app loading time. More specifically, we separated JavaScript parsing and made it run on different threads without violating the execution semantics of JavaScript. We also designed an efficient multi-threaded parsing architecture, which reduces the synchronization overhead and schedules the parsing requests appropriately. Finally, we explored two methods of choosing the target functions for concurrent parsing: one based on profiled information and the other based on speculative heuristics. We performed experiments on the WebKit browser with the JSC engine for real web apps. The result shows that the proposed concurrent parsing can improve the JavaScript performance during app loading by as much as 64% and by 39.7% on average. This improves the whole app loading performance tangibly, by as much as 32.7% and by 18.2%, on average.

Measuring Web Quality of Experience in Cellular Networks

“…They showed that their prioritization scheme improved the PLT by 50% to 60% on 1GHz CPU phones. reducing payload with code splitting [118], concurrent parsing [119], and compiled code reusing [119] have been proposed to optimize the JavaScript loading, parsing and execution process. A study by Park et al [119] shows that concurrent parsing improves the web application loading performance by up to 32.7% and by 18.2% on average.…”

An extensible approach for taming the challenges of JavaScript dead code elimination

“…In this section we use a purposefully-simple web app as running example for describing each phase of Lacuna. The HTML code of the web app just includes the example.js script in Listing 6. it defines the a, b, and c functions (lines [1][2][3][4][5][6][7][8][9][10][11][12][13][14] and calls a in line 15. When called, function a starts a 6-seconds timeout (lines 2-4), and function b prints a message in the browser console when the timeout is triggered (lines 3 and 7).…”

“…These nodes represent JavaScript functions that are not called by any other function (or from the global scope) according to all the different analysis techniques applied in the previous phase. We consider the global node as always alive since in JavaScript the global scope is always present and executed in a web app running in the browser 8 . By referring to Figure 3, in our running example the only node which is (i) different from the global one and (ii) unreachable from the global node is the one corresponding to the c function.…”

“…We developed those extensions with two purposes in mind: (i) to provide examples about how to extend Lacuna and (ii) to use them in empirically evaluation of Lacuna (see Section V). 8 http://www.w3schools.com/js/js function invocation.asp…”

“…Besides the obvious cost of increased file size (and network transfer time), there is an additional hidden cost to dead code: despite JavaScript dead code never being executed at run-time, it is still parsed by the JavaScript engine. This parsing overhead can take a significant portion of the complete interpretation time [8]. The costs for downloading and parsing dead code can negatively contribute to the loading time and energy consumption of web apps.…”