• Title/Summary/Keyword: Java Parallel Virtual Machine

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Running Large-scale Mobile Software using PDA Cluster Computing (PDA 클러스터 컴퓨팅을 활용한 대용량 모바일 소프트웨어 실행)

  • Min, Hye-Rhyn;Lee, Jong-Woo
    • Journal of Digital Contents Society
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    • v.10 no.2
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    • pp.249-258
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    • 2009
  • As wireless internet markets become larger than before, many mobile applications are also being developed actively. In this circumstances mobile devices such as cell phones, PDAs are playing an important role to satisfy the user's need of ubiquitous computing. Due to the hardware limitations, however, the mobile devices like PDA can not run large-scale softwares by itself. The main goal of this paper is to make large-scale applications runnable on PDA. To accomplish this, we used the PDA-JPVM cluster computing engine which has been already developed by us. We found out by running the applications and the performance evaluation that large-scale Java softwares can easily run on the hardware-limited PDA. And the performance evaluation results are also presented.

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Design of Translator for generating Secure Java Bytecode from Thread code of Multithreaded Models (다중스레드 모델의 스레드 코드를 안전한 자바 바이트코드로 변환하기 위한 번역기 설계)

  • 김기태;유원희
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 2002.06a
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    • pp.148-155
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    • 2002
  • Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.

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DEX2C: Translation of Dalvik Bytecodes into C Code and its Interface in a Dalvik VM

  • Kim, Minseong;Han, Youngsun;Cho, Myeongjin;Park, Chanhyun;Kim, Seon Wook
    • IEIE Transactions on Smart Processing and Computing
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    • v.4 no.3
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    • pp.169-172
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    • 2015
  • Dalvik is a virtual machine (VM) that is designed to run Java-based Android applications. A trace-based just-in-time (JIT) compilation technique is currently employed to improve performance of the Dalvik VM. However, due to runtime compilation overhead, the trace-based JIT compiler provides only a few simple optimizations. Moreover, because each trace contains only a few instructions, the trace-based JIT compiler inherently exploits fewer optimization and parallelization opportunities than a method-based JIT compiler that compiles method-by-method. So we propose a new method-based JIT compiler, named DEX2C, in order to improve performance by finding more opportunities for both optimization and parallelization in Android applications. We employ C code as an intermediate product in order to find more optimization opportunities by using the GNU C Compiler (GCC), and we will detect parallelism by using the Intel C/C++ parallel compiler and the AESOP compiler in our future work. In this paper, we introduce our DEX2C compiler, which dynamically translates Dalvik bytecodes (DEX) into C code with method granularity. We also describe a new method-based JIT interface in the Dalvik VM for the DEX2C compiler. Our experiment results show that our compiler and its interface achieve significant performance improvement by up to 15.2 times and 3.7 times on average, in Element Benchmark, and up to 2.8 times for FFT in Smartbench.