JavaScript Performance Research Articles

Methods for improving the performance of JavaScript interfaces

This article discusses various methods for improving the performance of JavaScript interfaces, which are critically important in the context of the rapid development of digital technologies. Effective, responsive interfaces not only attract and retain users, but also improve the position of websites in search engines and increase conversion rates. The research covers topics related to reducing the number of requests, asynchronous programming, optimizing data loading and caching. The article provides specific examples and tips, such as minimizing queries, using local variables to increase the speed of code execution, using event delegation methods and algorithmic optimization. Tools for diagnosing and fixing performance issues such as Lighthouse and Google PageSpeed Insights are also being considered. These recommendations and techniques have been tested and systematized by experienced developers to achieve the best results in improving JavaScript performance.

JavaScript Performance Tuning as a Crowdsourced Service

JavaScript Performance Tuning as a Crowdsourced Service

Dynamic code compression for JavaScript engine

AbstractWeb applications created using web languages such as HTML, CSS, and JavaScript are widely used regardless of execution environments, owing to their portability and popularity. Since JavaScript language is conventionally used for complex computations in large‐scale web apps, many optimization techniques have been proposed to accelerate the JavaScript performance. However, most of these optimizations speed up JavaScript engines at the expense of consuming more memory. Hence, memory consumption becomes an additional concern in the JavaScript field. Based on the research on memory status, we unearthed that a substantial portion of the heap memory is allocated for JavaScript source code, particularly in lightweight JavaScript engines, which may range from 13.2% to a maximal 52.5% of the entire heap. To resolve this memory issue, this article suggests a new memory optimization method, called dynamic code compression that dynamically compresses the source code and keeps its compressed form instead of the source string. A novel heuristic is proposed to timely compress the source code. We also re‐design the internal structure of the JavaScript engine to efficiently compress the source code without incurring any conflict. Using our code compression method, we could reduce the entire heap memory by up to 43.3% and consistently downsize the overall heap size. From an evaluation of standard benchmarks, our approach showed just 2.7% degradation in performance with negligible compression overhead, proving the feasibility of the code compression technique.

A Study of Timing Side-Channel Attacks and Countermeasures on JavaScript and WebAssembly

Side-channel attacks are a group of powerful attacks in hardware security that exploit the deficiencies in the implementation of systems. Timing side-channel attacks are one of the main side-channel attack categories that use the time difference of running an operation in different states. Many powerful attacks can be classified into this type of attack, including cache attacks. The limitation of these attacks is the need to run the spy program on the victim's system. Various studies have tried to overcome this limitation by implementing these attacks remotely on JavaScript and WebAssembly. This paper provides the first comprehensive evaluation of timing side-channel attacks on JavaScript and investigates challenges and countermeasures to overcome these attacks. Moreover, by investigating the countermeasures and their strengths and weaknesses, we introduce a detection-based approach, called Lurking Eyes. Our approach has the least reduction in the performance of JavaScript and WebAssembly. The evaluation results show that the Lurking eyes have an accuracy of 0.998, precision of 0.983, and F-measure of 0.983. Considering these values and no limitations, this method can be introduced as an effective way to counter timing side-channel attacks on JavaScript and WebAssembly. Also, we provide a new accurate timer, named Eagle timer, based on WebAssembly memory for implementing these attacks.

Reusing the Optimized Code for JavaScript Ahead-of-Time Compilation

As web pages and web apps increasingly include heavy JavaScript code, JavaScript performance has been a critical issue. Modern JavaScript engines achieve a remarkable performance by employing tiered-execution architecture based on interpreter, baseline just-in-time compiler (JITC), and optimizing JITC. Unfortunately, they suffer from a substantial compilation overhead, which can take more than 50% of the whole running time. A simple idea to reduce the compilation overhead is ahead-of-time compilation (AOTC), which reuses the code generated in the previous run. In fact, existing studies that reuse the bytecode generated by the interpreter or the machine code generated by the baseline JITC have shown tangible performance benefits [12, 31, 41]. However, there has been no study to reuse the machine code generated by the optimizing JITC, which heavily uses profile-based optimizations, thus not easily reusable. We propose a novel AOTC that can reuse the optimized machine code for high-performance JavaScript engines. Unlike previous AOTCs, we need to resolve a few challenging issues related to reusing profile-based optimized code and relocating dynamic addresses. Our AOTC improves the performance of a commercial JavaScript engine by 6.36 times (max) and 1.99 times (average) for Octane benchmarks, by reducing the compilation overhead and by running the optimized code from the first invocation of functions. It also improves the loading time of six web apps by 1.28 times, on average.

Concurrent JavaScript Parsing for Faster Loading of Web Apps

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.

JavaScript Parallelizing Compiler for Exploiting Parallelism from Data-Parallel HTML5 Applications

With the advent of the HTML5 standard, JavaScript is increasingly processing computationally intensive, data-parallel workloads. Thus, the enhancement of JavaScript performance has been emphasized because the performance gap between JavaScript and native applications is still substantial. Despite this urgency, conventional JavaScript compilers do not exploit much of parallelism even from data-parallel JavaScript applications, despite contemporary mobile devices being equipped with expensive parallel hardware platforms, such as multicore processors and GPGPUs. In this article, we propose an automatically parallelizing JavaScript compiler that targets emerging, data-parallel HTML5 applications by leveraging the mature affine loop analysis of conventional static compilers. We identify that the most critical issues when parallelizing JavaScript with a conventional static analysis are ensuring correct parallelization, minimizing compilation overhead, and conducting low-cost recovery when there is a speculation failure during parallel execution. We propose a mechanism for safely handling the failure at a low cost, based on compiler techniques and the property of idempotence. Our experiment shows that the proposed JavaScript parallelizing compiler detects most affine parallel loops. Also, we achieved a maximum speedup of 3.22 times on a quad-core system, while incurring negligible compilation and recovery overheads with various sets of data-parallel HTML5 applications.

Improving JavaScript performance by deconstructing the type system

Increased focus on JavaScript performance has resulted in vast performance improvements for many benchmarks. However, for actual code used in websites, the attained improvements often lag far behind those for popular benchmarks. This paper shows that the main reason behind this short-fall is how the compiler understands types. JavaScript has no concept of types, but the compiler assigns types to objects anyway for ease of code generation. We examine the way that the Chrome V8 compiler defines types, and identify two design decisions that are the main reasons for the lack of improvement: (1) the inherited prototype object is part of the current object's type definition, and (2) method bindings are also part of the type definition. These requirements make types very unpredictable, which hinders type specialization by the compiler. Hence, we modify V8 to remove these requirements, and use it to compile the JavaScript code assembled by JSBench from real websites. On average, we reduce the execution time of JSBench by 36%, and the dynamic instruction count by 49%.

Transitional Materialities and the Performance of JavaScript

This article approaches questions around materiality in digital writing practice in terms of performance, sound, and sound-poetry. Using Alan Golding's term 'Transitional Materiality' as a kind of model which connects my own practice-based project 'Sounding.js'. The article also considers the practice of the Language Poets and their exploration of materiality, performance and the sound of language in relation to the propositions of the Italian futurist Filippo Tommaso Marinetti. Other works discussed are Steve McCaffery's 'Carnival' and Christian Bok's 'Cyborg Opera'.

Using speculation to enhance javascript performance in web applications

JavaScript lets developers provide client-side interactivity in Web applications, but because it is sequential, it can't take advantage of multicore processors. Thread-level speculation (TLS) addresses this issue by enabling the speculation of JavaScript function calls and thus exploits the parallel performance potential multicore processors provide. The authors implemented TLS in the Squirrelfish JavaScript engine, which is part of the WebKit browser environment. They evaluate their approach using 15 popular Web applications on an eight-core computer, and show significant speed-ups without any modifications to the JavaScript source code.

Reuse of JIT compiled code based on binary code patching in JavaScript engine

JavaScript is a core language of web applications. As the most frequently used web language, it is used in more than 90% of web pages around the world. As a result, the performance of JavaScript en...

Fast and precise hybrid type inference for JavaScript

JavaScript performance is often bound by its dynamically typed nature. Compilers do not have access to static type information, making generation of efficient, type-specialized machine code difficult. We seek to solve this problem by inferring types. In this paper we present a hybrid type inference algorithm for JavaScript based on points-to analysis. Our algorithm is fast , in that it pays for itself in the optimizations it enables. Our algorithm is also precise , generating information that closely reflects the program's actual behavior even when analyzing polymorphic code, by augmenting static analysis with run-time type barriers. We showcase an implementation for Mozilla Firefox's JavaScript engine, demonstrating both performance gains and viability. Through integration with the just-in-time (JIT) compiler in Firefox, we have improved performance on major benchmarks and JavaScript-heavy websites by up to 50%. Inference-enabled compilation is the default compilation mode as of Firefox 9.