International Journal of Advanced Networking and Applications

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Image Processing on Agricultural Dataset Using Parallel Processing Based on Python


Affiliations
1 Doctoral Student of Computer Science, Universitas Gadjah Mada, Yogyakarta, Indonesia
2 Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta, Indonesia
 

Parallel processing divides data to be processed in each core on the CPU. One programming language that is widely used for machine learning applications and can accommodate the use of parallel processing is Python. Multiprocessing, mpi4py and CuPy are examples of Python libraries that can process data in parallel. In this research, we will compare the use of these libraries to process huge amounts of data. This research takes agricultural data for data image enhancement through color conversion from RGB to grayscale. The results showed that multiprocessing, mpi4py, and CuPy can increase the image enhancement speed three times faster than its single-core execution. Then, using a combination of multiprocessing with CuPy, a 1.7 times performance improvement is achieved compared with multiprocessing-only. Also, using a combination of mpi4py with CuPy achieved 2.5 times performance improvement compared to mpi4py-only.

Keywords

Core CPU, Parallel Processing, mpi4py, Multiprocessing, CuPy.
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  • Image Processing on Agricultural Dataset Using Parallel Processing Based on Python

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Authors

Faisal Dharma Adhinata
Doctoral Student of Computer Science, Universitas Gadjah Mada, Yogyakarta, Indonesia
Ahmad Ashari
Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta, Indonesia
Muhammad Alfian Amrizal
Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta, Indonesia

Abstract


Parallel processing divides data to be processed in each core on the CPU. One programming language that is widely used for machine learning applications and can accommodate the use of parallel processing is Python. Multiprocessing, mpi4py and CuPy are examples of Python libraries that can process data in parallel. In this research, we will compare the use of these libraries to process huge amounts of data. This research takes agricultural data for data image enhancement through color conversion from RGB to grayscale. The results showed that multiprocessing, mpi4py, and CuPy can increase the image enhancement speed three times faster than its single-core execution. Then, using a combination of multiprocessing with CuPy, a 1.7 times performance improvement is achieved compared with multiprocessing-only. Also, using a combination of mpi4py with CuPy achieved 2.5 times performance improvement compared to mpi4py-only.

Keywords


Core CPU, Parallel Processing, mpi4py, Multiprocessing, CuPy.

References