• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.111-117
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• 2018

Heterogeneous multi-core environment integrated with different functional cores is the powerful tool for the embedded system that became more complex and diverse. Specialized application requires one chip solution with different operating system over different cores. But this heterogeneity causes difficult configuration of the development environment, makes hard to develop and test software. We show the environment of heterogeneous multi-core processing can be mapped to symmetric multi-core environment. We construct Linux based RPMsg for the data exchange between processes similar with the heterogeneous multi-core RPMsg and experiment that the proposed environment can be used to reduce the steps of the heterogeneous multi-core application development. With this simplification, we suggest simulation method for easy development and debugging the heterogeneous multicore environment that makes complex steps to simple.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.407-414
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• 2017

Pig monitoring applications consisting of many tasks can take advantage of inherent data parallelism and enable parallel processing using performance accelerators. In this paper, we propose a task distribution method for pig monitoring applications into a heterogenous computing platform consisting of a multicore-CPU and a manycore-GPU. That is, a parallel program written in OpenCL is developed, and then the most suitable processor is determined based on the measured execution time of each task. The proposed method is simple but very effective, and can be applied to parallelize other applications consisting of many tasks on a heterogeneous computing platform consisting of a CPU and a GPU. Experimental results show that the performance of the proposed task distribution method on three different heterogeneous computing platforms can improve the performance of the typical GPU-only method where every tasks are executed on a deviceGPU by a factor of 1.5, 8.7 and 2.7, respectively.