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

This article describes a software environment called HybroGen, which helps to experiment binary code generation at run time. As computing architectures are getting more complex, the application performance is becoming data-dependent. The proposed experimental platform is helpful in programming applications that can be reconfigured at run time in order to be adapted for a new data environment. The HybroGen platform is adapted to heterogeneous architectures and can generate instructions for different targets. This platform allows to go farther than classical JIT compilation in many directions: the code generator is smaller by three orders of magnitude and faster by three orders of magnitude, compared to JIT (Just-In-Time) platforms, and allows making code transformation that is impossible in traditional compilation schemes, such as code generation for non von Neumann accelerators or dynamic code transformations for transprecision. The latter is illustrated in a code example: the square root with Newton’s algorithm. We also illustrate the proposed HybroGen platform with two other examples: a multiplication with a specialization on a value determined at run time, and a conversion of degrees Celsius to degrees Fahrenheit. This article presents a proof of concept of the proposed HybroGen platform in terms of its functionalities, and demonstrates the working status.

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“…JavaScript also uses a JIT compilation strategy, but both Java and Javascript have a very costly compilation phase as described in [10].…”

Section: High Level Intermediate Representationmentioning

confidence: 99%

Dynamic Compilation for Transprecision Applications on Heterogeneous Platform

Dumas

Charles

Mambu

et al. 2021

JLPEA

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“…warmup time. Park et al[16] even keep the compiled code from previous runs and solve issues for code relocation and validating speculations.…”

mentioning

confidence: 99%

Profile Guided Offline Optimization of Hidden Class Graphs for JavaScript VMs in Embedded Systems

Ugawa

Marr

Jones

2022

Proceedings of the 14th ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages

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JavaScript is increasingly used for the Internet of Things (IoT) on embedded systems. However, JavaScript's memory footprint is a challenge, because normal JavaScript virtual machines (VMs) do not fit into the small memory of IoT devices. In part this is because a significant amount of memory is used by hidden classes, which are used to represent JavaScript's dynamic objects efficiently.In this research, we optimize the hidden class graph to minimize their memory use. Our solution collects the hidden class graph and related information for an application in a profiling run, and optimizes the graph offline. We reduce the number of hidden classes by avoiding introducing intermediate ones, for instance when properties are added one after another. Our optimizations allow the VM to assign the most likely final hidden class to an object at its creation. They also minimize re-allocation of storage for property values, and reduce the polymorphism of inline caches.We implemented these optimizations in a JavaScript VM, eJSVM, and found that offline optimization can eliminate 61.9% of the hidden classes on average. It also improves execution speed by minimizing the number of hidden class transitions for an object and reducing inline cache misses.CCS Concepts: • Software and its engineering → Virtual machines.

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Os noise mitigations for benchmarking web browser execution environment performance

Toufie,

Kabaso

2024

Discov Computing

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Web browsers have almost since birth sought to host and run feature-rich, compute-intensive and complex applications, within their execution environments (EEs), thereby achieving performance akin to native desktop applications. Studies that have proposed performance improvements to these Web browser EEs together with their respective benchmarks have endeavoured to use proven methodologies with which they confirmed the scientific rigour of those benchmarks, but have often overlooked operating system (OS) noise and the negative impact that it has on benchmarks. This paper identifies the various types of OS noise that exist within a typical Intel-based computer system that utilises a Linux-based OS. While also reviewing what related studies have done to mitigate against them. A series of mitigations were then proposed for the Linux-based system that was then benchmarked using the PolyBench/C benchmarking suite. Whilst not all OS noise was mitigated against, our results confirm that the OS noise mitigations that were provided, significantly reduced OS noise. Further significance of this study is that a more controlled benchmarking environment is made available, due to focusing the CPU processing almost entirely on the benchmarks only, while also enhancing the precision and accuracy of the benchmarks. In this study, we lay down a foundation that will make future Web browser EE as well as any other type of computer software benchmarking more precise and accurate, thereby improving the scientific rigour of those benchmarks.

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

Cited by 4 publications

References 20 publications

Dynamic Compilation for Transprecision Applications on Heterogeneous Platform

Dynamic Compilation for Transprecision Applications on Heterogeneous Platform

Profile Guided Offline Optimization of Hidden Class Graphs for JavaScript VMs in Embedded Systems

Os noise mitigations for benchmarking web browser execution environment performance

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