International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 07 | July 2024
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p-ISSN: 2395-0072
Review on GPU Architecture YASH GOYAL Electronics and Communication Engineering, Netaji Subash University of Technology, Delhi -----------------------------------------------------------------------------***-------------------------------------------------------------------------
Abstract - Graphics Processing Units (GPUs) have undergone a transformative evolution from fixed-function devices designed
solely for graphics rendering to highly programmable and parallel processors capable of handling a diverse array of computationally intensive tasks. This review paper provides a comprehensive examination of the architectural developments and applications of GPUs. Key milestones in GPU evolution, such as the introduction of programmable shaders and unified shader architectures, are discussed alongside modern advancements in GPU technology. The paper explores various applications of GPUs, including high-performance computing, artificial intelligence, machine learning, and cryptocurrency mining. Additionally, the review delves into the development environments and support systems that facilitate GPU programming, highlighting essential tools, libraries, and integrated development environments. Finally, the paper addresses the challenges faced by current GPU architectures, such as power consumption and the future of heterogeneous computing, projecting the continued integration of AIspecific hardware in GPUs. Through this detailed exploration, the paper underscores the pivotal role GPUs play in advancing computational capabilities across multiple domains. Key Words: GPU architecture, programmable shaders, unified shader architecture, high-performance computing (HPC), artificial intelligence (AI), machine learning, cryptocurrency mining, CUDA, OpenCL, tensor cores, development environments, GPU-accelerated computing, power efficiency, heterogeneous computing, NVIDIA Tesla, parallel processing, deep learning
1.Introduction Graphics Processing Units (GPUs) have revolutionized the field of computing, providing unprecedented parallel processing power that has transformed graphics rendering and general-purpose computing alike. Initially designed as dedicated hardware for accelerating the rendering of images and videos, GPUs have evolved into versatile processors capable of executing a wide array of computational tasks with high efficiency and speed. This evolution has been driven by significant advancements in GPU architecture, including the transition from fixed-function pipelines to programmable shaders, the development of unified shader architectures, and the integration of specialized cores for artificial intelligence (AI) and machine learning (ML) applications. The demand for high-performance computing (HPC) has surged across various industries, from scientific research and data analysis to entertainment and healthcare. GPUs have become a cornerstone of HPC due to their ability to handle large-scale parallel computations, significantly outperforming traditional Central Processing Units (CPUs) in tasks that require extensive data processing and complex calculations. This capability has made GPUs indispensable in fields such as climate modeling, molecular dynamics, and astrophysics, where simulations and data processing need to be executed with high precision and speed. In the realm of artificial intelligence and machine learning, GPUs have played a pivotal role in accelerating the training and inference of deep neural networks. The introduction of tensor cores in modern GPUs has specifically enhanced their performance in AI tasks, enabling faster and more efficient processing of large datasets. This has led to significant advancements in AI research and applications, including natural language processing, image recognition, and autonomous systems. Moreover, the versatility of GPUs extends to emerging applications such as cryptocurrency mining and real-time data processing in autonomous vehicles. The parallel processing capabilities of GPUs make them ideal for the repetitive and computationally intensive tasks involved in mining cryptocurrencies. In autonomous vehicles, GPUs process sensor data and run complex algorithms for perception, planning, and control, ensuring safe and efficient operation.
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