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All Journal Dinamik EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Sistemasi: Jurnal Sistem Informasi JURNAL ILMIAH OBSGIN : Jurnal Ilmiah Ilmu Kebidanan & Kandungan Jurnal Pendidikan dan Teknologi Indonesia Respiratory Science Malcom: Indonesian Journal of Machine Learning and Computer Science COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Media Penelitian dan Pengembangan Kesehatan SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Amarudin, Amarudin
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Transportation

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Performance Evaluation of CPU and GPU Architectures in Google Colab-Based Parallel Computing Using Execution Time and Speedup Haryanto, Didik; Hoiri, Muhamad Nur; Sidiq, Ahmad; Azzahrah, Amelia; Amarudin, Amarudin
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15758

Abstract

Modern computing demands have driven the use of processing devices capable of handling large workloads quickly and efficiently. The CPU plays an important role as the central controller for general-purpose instructions, whereas the GPU provides parallel-processing capabilities that are better suited to large-scale numerical operations. This study aims to evaluate performance differences between CPU and GPU architectures in Google Colab-based parallel computing. A quantitative experimental method was employed using a matrix multiplication test scenario implemented with the PyTorch library. Tests were performed on five matrix sizes: 500 x 500, 1000 x 1000, 2000 x 2000, 3000 x 3000, and 4000 x 4000. The measured parameters included CPU execution time, GPU execution time, and speedup. The results show that the GPU was not optimal for the small 500 x 500 matrix, with a speedup of 0.47 times. However, for larger matrices, the GPU delivered substantial performance gains, achieving speedups of 23.25 to 27.30 times over the CPU. These findings indicate that GPUs are more effective for large-scale parallel processing.
Co-Authors Abdul Kholiq Adrianison, Adrianison Agshari, M. Faisal Ahmad Sidiq Azzahrah, Amelia Chintara Wanda, Aulia Esha, Indi Fauzi Fauzi Haryanto, Didik Hoiri, Muhamad Nur Indra Richardo, Kelvin Inonu, Onassis Yusuf Inzaghi, M. Rana Magda, Kardita Neneng Neneng Putra, Rulyansyah Permata Rahardiantini, Ikha Simatupang, Elvando Tunggul Mauliate Sri Melati Munir Susanto, Erliyan Redy Wijaya, Dewi Yanti, Masyitah Novia