The Study of Android Parallel Programming Based on the Dual-Core Cortex-A9

Wu, Chien-Chung; Huang, Jyun-Jie

doi:10.1109/iih-msp.2013.124

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…In 2013 Chien-Chung Wu and Juyn-Jie Huang published a study implementing Android parallel programming based on the dual-core Cortex A9 [13]. This study focused on single node multicore programming with OpenMP rather than cluster computing with MPI.…”

Section: Previous Approachesmentioning

confidence: 99%

High Performance Computing Over Parallel Mobile Systems

Attia¹,

El-Korany²,

Moussa³

2016

ijacsa

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Abstract-There are currently more mobile devices than people on the planet. This number is likely to multiply many folds with the Internet of Things revolution in the next few years. This may treasure an unprecedented computational power especially with the wide spread of multicore processors on mobile phones. This paper investigates and proposes a new methodology for mobile cluster computing, where multiple mobile devices including their multicore processors can be combined to perform possibly massively parallel applications. The paper presents in details the steps for building and testing the mobile cluster using the proposed methodology and proving the successful implementation.

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Section: Previous Approachesmentioning

confidence: 99%

High Performance Computing Over Parallel Mobile Systems

Attia¹,

El-Korany²,

Moussa³

2016

ijacsa

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“…Therefore, we can apply these multi-thread methods to implement dynamic simulations with CPU multi-core approach [14,15].…”

Section: Multi-thread In Mobile Devicementioning

confidence: 99%

Plausible mass-spring system using parallel computing on mobile devices

Hong

Jeon²,

Yum³

et al. 2016

Hum. Cent. Comput. Inf. Sci.

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Recently the hardware performance of mobile devices have been extremely increased and advanced mobile devices provide multi-cores and high clock speed. In addition, mobile devices have advantages in mobility and portability compared with PC and Console, so many games and simulation programs have been developed under mobile environments. Physically-based simulation is a one of the key issues for deformable object modeling which is widely used to represent the realistic expression of 3D soft objects with tetrahedrons for game and 3D simulation. However, it requires high computation power to plausibly and realistically represent the physical behaviors and interactions of deformable objects. In this paper, we implemented parallel cloth and mass-spring simulation using graphics processing unit (GPU) with OpenCL and multi-threaded central processing unit (CPU) on a mobile device. We applied CPU and GPU parallel computing technique into spring force computation and integration methods such as Euler, Midpoint, 4th-order Runge-Kutta to optimize the computational burden of dynamic simulation. The integration methods compute the next step of positions and velocities in each node. In this paper, we tested the performance analysis for the spring force calculation and integration method process using CPU only, multi-threaded CPU, and GPU on mobile device respectively. Our experimental results concluded that the calculation using proposed multi-threaded CPU and GPU multi-threaded CPU are much faster than using just the CPU only.

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CBVMI: A container-based virtualization method for instruments

Huang

Liu

2018

Measurement

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The Study of Android Parallel Programming Based on the Dual-Core Cortex-A9

Cited by 3 publications

References 10 publications

High Performance Computing Over Parallel Mobile Systems

High Performance Computing Over Parallel Mobile Systems

Plausible mass-spring system using parallel computing on mobile devices

CBVMI: A container-based virtualization method for instruments

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