eJSTK: Building JavaScript virtual machines with customized datatypes for embedded systems

Ugawa, Tomoharu; Iwasaki, Hideya; Kataoka, Takafumi

doi:10.1016/j.cola.2019.01.003

Cited by 10 publications

(16 citation statements)

References 7 publications

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“…11 as a representative program using a superinstruction with a string constant operand. The SunSpider benchmarks were modified in the same way as in the article [2]: the number of iterations were adjusted to be suitable for the evaluation of eJSVM, for example.…”

Section: Discussionmentioning

confidence: 99%

“…eJS [1], [2] is a JavaScript system that has a mechanism to reduce the VM footprint by generating specialized VMs for individual applications. In particular, the VM generator of eJS, eJSTK, specializes VM instructions to their operand datatypes.…”

Section: Overviewmentioning

confidence: 99%

“…A variety of devices with poor processors and small memories for their sensor and communication facilities have come to be available for the Internet of Things (IoT). To support software development for these devices, some JavaScript VMs for embedded systems, such as eJS [1], [2], JerryScript * 1 , and mJS * 2 , have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…Rather, they are implemented as pure interpreters. This means they are slower executing than JavaScript VMs working in Web browsers [2]. Nevertheless, execution speed is important even for embedded systems.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of Superinstructions for JavaScript Virtual Machine Generation System for Embedded Systems eJSTK

Nonaka

Ugawa

2019

Journal of Information Processing

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Embedded systems generally have a small amount of memory and slow CPUs. Therefore it is desirable to increase the speed of JavaScript virtual machines (VMs) without increasing memory footprint. In this research, we introduce superinstructions, combinations of constant load instructions and arithmetic, logical, and relational (ALR) instructions to increase execution speed. Introducing superinstructions increases the size of a VM. Thus, we designed superinstructions so that they would share their implementation code with the ALR instructions, from which they are made. Furthermore, we simplified their type-based dispatching code through specialization to the datatypes of their constant operands. We developed a VM generator that creates VMs that have superinstructions in accordance with this approach and a compiler that compiles JavaScript programs using them.

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Section: Discussionmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of Superinstructions for JavaScript Virtual Machine Generation System for Embedded Systems eJSTK

Nonaka

Ugawa

2019

Journal of Information Processing

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“…However, these calculations are theoretical. In practice, a solution can be represented as an object (in object oriented languages such as Java [48,49]) that has additional data that requires memory. In these cases, memory consumption is higher.…”

mentioning

confidence: 99%

Long Term Memory Assistance for Evolutionary Algorithms

et al. 2019

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Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50 % more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F 03 and F 05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. Finally, LTMA was implemented under a generalized and extendable open source system, called EARS. Any EA researcher could apply LTMA to a variety of optimization problems and evolutionary algorithms, either existing or new ones, in a uniform way.

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Collecting Type Information Using Unit Tests for Customizing JavaScript Virtual Machines

Ugawa

Iwasaki

Kataoka

2019

Proceedings of the 14th Workshop on Implementation, Compilation, Optimization of Object-Oriented Languages, Programs and System

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eJSTK: Building JavaScript virtual machines with customized datatypes for embedded systems

Cited by 10 publications

References 7 publications

Design and Implementation of Superinstructions for JavaScript Virtual Machine Generation System for Embedded Systems eJSTK

Design and Implementation of Superinstructions for JavaScript Virtual Machine Generation System for Embedded Systems eJSTK

Long Term Memory Assistance for Evolutionary Algorithms

Collecting Type Information Using Unit Tests for Customizing JavaScript Virtual Machines

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