A study on Multiple-Instance GPU, Evolution, Architecture and Applications
Abstract
Multiple Instance GPU (MIG) is a promising
technology for improving the efficiency and scalability of GPUbased systems. MIG allows multiple users or workloads to share a single physical GPU while maintaining performance and resource isolation. This study covers the evolution of GPUs, the architecture of MIG, and various applications of MIG, including virtualization, deep learning inference, and high-performance computing. The study also discusses the architectural considerations of MIG for scalable GPU virtualization and the challenges in implementing MIG in real-world systems. The references provided offer further insights into the technical details of MIG and its potential for improving the performance and cost-effectiveness of GPU-based systems.
Keywords:
Graphics Processing Unit (GPU, Multiple Instance GPU (MIG), GPU virtualization, deep learning inference, high performance computing
Published
Issue
Section
License
Copyright (c) 2023 International Journal on Emerging Research Areas
This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- B Bidhun, Deepak Dayanandan, Joel Joy, Vargheese Francis, Vani V Prakash, A Comprehensive Review of Lightweight and Attention-Driven Deep Learning Models for Automated Cataract Detection , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Alan K George, Arpita Mary Mathew, Asin Mary Jacob, Elizabeth Antony, Shiney Thomas, Lung Cancer Subtype Classification Using Deep Learning Models , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Albin , Aarunya Retheep, Adona Shibu, Athul P Shibu, Lis Jose, LanguaGuide -Your personalized AI companion for mastering languages, anytime, anywhere. , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Honey Joseph, A Survey and Analysis on Predicting Heart Disease Using Machine Learning Techniques , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Mrs. Lis Jose, Akhil Lorence, Akhil Manohar, Amal Jose Chacko, Arjun J, Lung Disease Detection From Chest X-ray Images Using Hybrid Machine Learning Model , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Fabeela Ali Rawther, Akhil P Dominic, Alan James, Christy Chacko, Elena Maria Varghese, Early Detection of Attention Deficiency Using ML , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Betzy Babu Thoppil, Anugrah Premachandran, Annapoorna M, Ashwin Mathew Zachariah, Bala Susan Jacob, Advanced Sensor-Based Landslide Detection and Alert System Utilizing Machine Learning , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Aniruddha Das, Avisikta Modak, The Carbon footprint of Machine Learning Models , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Dona S Plavelil, A Devanandha, Haritha H Kurupp, Jissin k Jose, DETECTION OF ALZHEIMER’S DISEASE AND ASSISTANCE , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Jacob George, Jerin Xavier, Jovin J George, Joyel Xavier, Subini Therese Babu, Pharmaceutical Sales Forecasting using Machine Learning , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
You may also start an advanced similarity search for this article.