Glossary of key terms associated with GPU chips:
Here’s a glossary of key terms associated with GPU chips:
GPU (Graphics Processing Unit)
A specialized processor designed to accelerate rendering graphics and handle parallel processing tasks, commonly used in gaming, video rendering, and AI computations.CUDA (Compute Unified Device Architecture)
A parallel computing platform and programming model developed by NVIDIA that allows developers to use the GPU for general-purpose computing tasks.Parallel Processing
The simultaneous data processing using multiple processors or cores to handle large sets of data or complex computations more efficiently.Tensor Cores
Specialized cores within modern GPUs, such as NVIDIA's Volta and Turing architectures, optimized for performing deep learning and AI workloads, specifically matrix operations.VRAM (Video RAM)
A type of memory used to store image data for fast access by the GPU during rendering and video processing. It helps in enhancing performance in graphical tasks.Ray Tracing
A rendering technique used to simulate realistic lighting, shadows, and reflections by tracing the path of light rays in a 3D scene.GPU Architecture
The design and layout of a GPU, which dictates its performance, efficiency, and how it handles various tasks like rendering, computation, and parallel processing.GPU Acceleration
The use of a GPU to speed up computations traditionally handled by a CPU, particularly in tasks like data analysis, AI model training, and scientific simulations.DLSS (Deep Learning Super Sampling)
An AI-based image enhancement technology developed by NVIDIA that uses deep learning to upscale lower-resolution images in real-time to improve performance and visual quality in games.GPU Rendering
The process of generating images, videos, and animations using the GPU's parallel processing power, typically used in graphic design, video production, and gaming.Shader Cores
Cores within a GPU that execute shaders, which are small programs responsible for rendering effects like lighting, textures, and shading in 3D graphics.FP32 (Floating Point 32-bit)
A standard for representing numbers in a 32-bit floating-point format. It is used in computations, such as graphics rendering and deep learning model training.FP64 (Floating Point 64-bit)
A 64-bit version of floating-point representation, often used for high-precision computations, such as scientific simulations or complex financial modeling.GPUs for AI (Artificial Intelligence)
GPUs are increasingly used for AI-related tasks, like training deep learning models, due to their ability to handle large amounts of data and perform complex matrix operations in parallel.TensorFlow (for GPU)
An open-source machine learning framework by Google that can be accelerated using GPUs for faster computation in training AI models.GPU Driver
Software that allows the operating system and applications to communicate with the GPU, ensuring proper functionality and performance.NVIDIA
A leading company in the GPU market, known for its GeForce gaming GPUs and Tesla and Quadro GPUs used in data centers, AI, and professional applications.AMD (Advanced Micro Devices)
A semiconductor company that competes with NVIDIA, offering GPUs like the Radeon series, used for gaming, workstation, and compute applications.Intel Xe Graphics
A brand of integrated and discrete GPUs developed by Intel, designed to compete with NVIDIA and AMD in both consumer and enterprise markets.VR (Virtual Reality)
An immersive computing experience that typically requires powerful GPUs to render 3D environments and interactive elements in real-time.Compute Shaders
Shaders that allow GPUs to perform general-purpose computations outside of rendering, such as physics simulations and AI model inference.GPU Compute Units
The individual processing units within a GPU that execute computational tasks. The number of compute units directly affects the GPU's overall performance.Graphics Pipeline
A series of steps used to transform 3D models into 2D images. It includes stages like vertex processing, rasterization, and fragment shading, typically handled by the GPU.CUDA Cores
The basic processing units within an NVIDIA GPU, responsible for executing parallel computations. More CUDA cores usually lead to higher GPU performance.GPU Overclocking
The process of increasing the clock speed of the GPU to boost its performance. This is typically done for gaming or computational workloads but can increase power consumption and heat.HPC (High-Performance Computing)
The use of powerful computers and specialized GPUs to perform complex simulations, modeling, and data analysis in fields like scientific research and engineering.Deep Learning
A subset of machine learning involving neural networks with many layers that require large computational resources, often accelerated by GPUs.Machine Learning
A type of AI that allows systems to learn and make decisions based on data, with GPU acceleration often used to train complex models faster.GPGPU (General-Purpose GPU)
Refers to the use of GPUs to perform computations traditionally handled by CPUs, such as in scientific simulations, financial modeling, and image processing.GPU Cluster
A group of GPUs connected together to work on a specific task, often used in AI research and large-scale data processing.NVIDIA RTX
A series of high-performance GPUs from NVIDIA, featuring ray tracing cores for real-time ray tracing and AI-driven features like DLSS.AMD Radeon
A brand of GPUs developed by AMD, designed for gaming, creative professionals, and scientific applications.GPU Memory Bandwidth
The rate at which data can be transferred between the GPU and VRAM, a key factor in GPU performance, particularly in memory-intensive tasks like rendering and AI inference.GPUs for Cryptocurrency Mining
GPUs are used in cryptocurrency mining for tasks like solving complex algorithms, with their parallel processing power enabling faster mining operations.Energy Efficiency in GPUs
The ability of a GPU to perform computational tasks while consuming as little energy as possible, important for reducing operational costs and environmental impact.Ray Tracing Cores
Specialized hardware within GPUs designed to accelerate real-time ray tracing, enabling more realistic lighting, shadows, and reflections in graphics rendering.Multi-GPU Setup
A configuration where multiple GPUs are used in parallel to enhance performance in computational tasks, often used in gaming, AI, and scientific computing.SLI (Scalable Link Interface)
A technology developed by NVIDIA that allows multiple GPUs to work together to improve performance in supported applications, such as games and 3D rendering.CrossFire
A multi-GPU technology developed by AMD that enables the use of two or more GPUs in tandem to increase graphics processing power.Gaming GPUs
GPUs optimized for gaming applications, designed to handle high frame rates and complex graphics with minimal latency.GPU Workloads
The specific tasks or computational demands that a GPU is designed to handle, such as rendering, AI model training, or scientific simulations.Parallel Computing
A computational approach that splits tasks into smaller chunks and processes them simultaneously across multiple processors or cores, often used with GPUs for speed.Performance Scaling
The ability of a system, particularly in multi-GPU setups, to increase performance as more GPUs or computational resources are added.VRAM Clock Speed
The speed at which VRAM operates, affecting the rate at which data is read and written, which in turn influences the GPU's rendering performance.Tensor Processing Units (TPUs)
Specialized hardware developed by Google to accelerate machine learning tasks, particularly for deep learning models, often compared to GPUs for AI workloads.DL (Deep Learning) Optimization
The process of improving the performance of deep learning models through techniques like training optimization, algorithm improvements, and efficient hardware usage (e.g., GPUs).GPU Computing Frameworks
Software tools and libraries that help developers harness the power of GPUs for general-purpose computing, such as CUDA, OpenCL, and DirectCompute.Edge Computing
A distributed computing paradigm that brings computation and data storage closer to the data source (e.g., IoT devices), often relying on GPUs to process data locally for faster response times.GPU in Data Centers
Data centers use GPUs to accelerate workloads, especially for AI, machine learning, and high-performance computing tasks, reducing latency and improving computational efficiency.AI Model Training on GPUs
The process of using GPUs to accelerate the training of machine learning and deep learning models, where their parallel processing capabilities provide a significant performance boost.
This glossary provides insights into the key terminology related to GPUs, helping you understand their role in modern computing, gaming, AI, and more.