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
- Nikita Niteen , Juby Mathew, Securing AI: Understanding and Defending Against Adversarial Attacks in Deep Learning Systems , International Journal on Emerging Research Areas: Vol. 3 No. 2 (2023): IJERA
- Aaron Samuel Mathew, Green Cloud Computing: A Literature Survey , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Alan K George, Arpita Mary Mathew, Asin Mary Jacob, Elizabeth Antony, Shiney Thomas, Classification of Lung Cancer Subtypes Using Deep Learning Model , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- K.M Gishma, K.B Annmaria , V.N Ramna Parvan , Anagha Suresh, Athira Shaji, LIP READING AND PREDICTION SYSTEM BASED ON DEEP LEARNING , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- M Manoj, A S Athira, Rishna Ramesh, Sandhra Gopi, Firoz P U, Smart Attend Insights , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Sebastian Biju, Samuel Michael, Thomas Mathew Jose, Mathew Abraham, Minu Cherian, A Review of Machine Learning Approaches for Canine Skin Disease Detection Using Image Processing Techniques , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Amal P Varghese, Simy Mary Kurian, Advancements in ECG Heartbeat Classification: A Comprehensive Review of Deep Learning Approaches and Imbalanced Data Solutions , International Journal on Emerging Research Areas: Vol. 3 No. 2 (2023): IJERA
- Ansamol Varghese, Anandhu Anoj, Emil Thomas, Deepta K Sunny, Angel Thomas, TrueNews: AI Powered Detection of Manipulated Text and Images , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Parvathy V A, Irfana Parveen C A, Alisha K A, Reshma P R, Manu Krishna C P, Detection of Diabetic Retinopathy and Glaucoma using Deep Learning , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Richa Maria Biju, Merwin Maria Antony, Mishal Rose Thankachan, Joshua John Sajit, Bini M Issac, Enhancing Image Forgery Detection with Multi-Modal Deep Learning and Statistical Methods , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
You may also start an advanced similarity search for this article.