The document discusses graphics processing units (GPUs). It begins with an introduction and definition of GPUs as processors designed specifically for processing 3D graphics. It then covers the components of a GPU and compares GPU and CPU architectures. Specifically, it notes that GPUs have many parallel execution units while CPUs have few, and that GPUs have significantly faster memory interfaces than CPUs. The document concludes by noting that GPU development is ongoing and faster GPUs can be expected in the future.
Graphics Processing Units (GPUs) are specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. GPUs have evolved from simple video controllers to massively parallel multi-core processors capable of general purpose computing. Modern GPUs use a single-instruction, multiple-thread (SIMT) architecture and have a parallel programming model that maps computations to thousands of threads to maximize throughput. Programming frameworks like CUDA and OpenCL allow general purpose programming on GPUs by mapping algorithms to their highly parallel structure.
A GPU is a specialized microprocessor that accelerates 3D and 2D graphics. It was defined in 1999 by Nvidia who marketed the first GPU chip. GPUs are efficient at manipulating and displaying computer graphics compared to CPUs. The GPU pipeline receives geometry from the CPU and provides pictures as output. It processes through stages of vertex processing, triangle setup, pixel processing, and writing to memory interfaces like frame buffers and textures. GPUs are used widely in applications like gaming, CAD tools, and computer graphics due to their powerful graphics processing capabilities.
The document discusses the evolution of GPU architecture and capabilities over time. It describes how GPUs have become massively parallel processors with programmable capabilities beyond just graphics. The document outlines the core components of a GPU including the graphics pipeline and programming model. It also discusses how GPUs are well suited for parallel, data-intensive applications and how their capabilities have expanded into general purpose computing through technologies like CUDA.
The document discusses graphics processing units (GPUs). It provides three key points:
1. A GPU is a specialized microprocessor that accelerates the rendering of 2D and 3D graphics. It allows for faster drawing of polygons, textures, and images to improve rendering speed.
2. GPUs were originally developed in the 1990s by Nvidia and helped popularize 3D graphics. Modern GPUs contain hundreds of processing cores and have memory bandwidths up to 230GB/s.
3. GPUs are used across many devices from embedded systems and mobile phones to PCs and game consoles. They accelerate graphics tasks through parallel processing compared to conventional CPUs.
The document provides an overview of graphics processing units (GPUs). It defines a GPU as a processor optimized for graphics, video, and visual computing. GPUs have a highly parallel architecture with thousands of smaller cores designed to handle multiple tasks simultaneously, unlike CPUs which have fewer serial cores. The document compares CPU and GPU architectures, describes the physical components of a GPU including the motherboard, graphics processor, memory, and display connector. It provides details on GPU memory, pipelines, and manufacturers like NVIDIA, AMD, and Intel. The document concludes with information on latest GPU technologies such as CUDA, PhysX, 3D Vision, and examples of high-end consumer GPUs.
Presentation on graphics processing unit (GPU)MuntasirMuhit
The document discusses graphics processing units (GPUs). It explains that GPUs have hundreds of cores compared to CPUs which have 4-8 cores. GPUs are designed to efficiently handle parallel tasks like 3D rendering that require large computational workloads. The architecture of GPUs is optimized for parallel processing with features like independent operations and streaming memory access. The document concludes that GPUs can provide improved performance, energy efficiency, and reduced energy consumption compared to CPUs for data parallel scientific applications.
The document discusses GPU architecture and computing. It describes how GPUs have evolved from specialized graphics processors into general parallel computing devices. The document outlines GPU architecture including the graphics pipeline, use of shader programs, and SPMD programming model. It also discusses virtualizing GPUs through front-end and back-end approaches to enable sharing of physical GPUs across VMs.
This document discusses graphics processing units (GPUs) and GPU rendering. It describes how GPUs assist CPUs in performing complex rendering calculations more quickly. The document outlines some of the latest and most powerful GPU models from AMD and Nvidia, such as the Radeon R9 295X2 and GeForce GTX Titan Z. It also discusses challenges in GPU rendering like realistic lighting simulations and high power consumption. GPU-accelerated computing is increasing performance across many applications by offloading compute-intensive tasks to thousands of GPU cores.
Graphics Processing Units (GPUs) are specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. GPUs have evolved from simple video controllers to massively parallel multi-core processors capable of general purpose computing. Modern GPUs use a single-instruction, multiple-thread (SIMT) architecture and have a parallel programming model that maps computations to thousands of threads to maximize throughput. Programming frameworks like CUDA and OpenCL allow general purpose programming on GPUs by mapping algorithms to their highly parallel structure.
A GPU is a specialized microprocessor that accelerates 3D and 2D graphics. It was defined in 1999 by Nvidia who marketed the first GPU chip. GPUs are efficient at manipulating and displaying computer graphics compared to CPUs. The GPU pipeline receives geometry from the CPU and provides pictures as output. It processes through stages of vertex processing, triangle setup, pixel processing, and writing to memory interfaces like frame buffers and textures. GPUs are used widely in applications like gaming, CAD tools, and computer graphics due to their powerful graphics processing capabilities.
The document discusses the evolution of GPU architecture and capabilities over time. It describes how GPUs have become massively parallel processors with programmable capabilities beyond just graphics. The document outlines the core components of a GPU including the graphics pipeline and programming model. It also discusses how GPUs are well suited for parallel, data-intensive applications and how their capabilities have expanded into general purpose computing through technologies like CUDA.
The document discusses graphics processing units (GPUs). It provides three key points:
1. A GPU is a specialized microprocessor that accelerates the rendering of 2D and 3D graphics. It allows for faster drawing of polygons, textures, and images to improve rendering speed.
2. GPUs were originally developed in the 1990s by Nvidia and helped popularize 3D graphics. Modern GPUs contain hundreds of processing cores and have memory bandwidths up to 230GB/s.
3. GPUs are used across many devices from embedded systems and mobile phones to PCs and game consoles. They accelerate graphics tasks through parallel processing compared to conventional CPUs.
The document provides an overview of graphics processing units (GPUs). It defines a GPU as a processor optimized for graphics, video, and visual computing. GPUs have a highly parallel architecture with thousands of smaller cores designed to handle multiple tasks simultaneously, unlike CPUs which have fewer serial cores. The document compares CPU and GPU architectures, describes the physical components of a GPU including the motherboard, graphics processor, memory, and display connector. It provides details on GPU memory, pipelines, and manufacturers like NVIDIA, AMD, and Intel. The document concludes with information on latest GPU technologies such as CUDA, PhysX, 3D Vision, and examples of high-end consumer GPUs.
Presentation on graphics processing unit (GPU)MuntasirMuhit
The document discusses graphics processing units (GPUs). It explains that GPUs have hundreds of cores compared to CPUs which have 4-8 cores. GPUs are designed to efficiently handle parallel tasks like 3D rendering that require large computational workloads. The architecture of GPUs is optimized for parallel processing with features like independent operations and streaming memory access. The document concludes that GPUs can provide improved performance, energy efficiency, and reduced energy consumption compared to CPUs for data parallel scientific applications.
The document discusses GPU architecture and computing. It describes how GPUs have evolved from specialized graphics processors into general parallel computing devices. The document outlines GPU architecture including the graphics pipeline, use of shader programs, and SPMD programming model. It also discusses virtualizing GPUs through front-end and back-end approaches to enable sharing of physical GPUs across VMs.
This document discusses graphics processing units (GPUs) and GPU rendering. It describes how GPUs assist CPUs in performing complex rendering calculations more quickly. The document outlines some of the latest and most powerful GPU models from AMD and Nvidia, such as the Radeon R9 295X2 and GeForce GTX Titan Z. It also discusses challenges in GPU rendering like realistic lighting simulations and high power consumption. GPU-accelerated computing is increasing performance across many applications by offloading compute-intensive tasks to thousands of GPU cores.
This document discusses GPU architecture and functioning. It defines a GPU as a processor optimized for graphics, video and visual computing. GPUs are highly parallel and multithreaded. The document outlines the evolution of GPUs from specialized graphics cards to massively parallel processors capable of general purpose computing. It describes the GPU pipeline including host interface, vertex processing, triangle setup, fragment processing and memory interface. It also discusses programming models like CUDA and differences between CPUs and GPUs.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
A GPU is an electronic circuit that rapidly manipulates memory to accelerate image processing and display. Modern GPUs use parallel processing, rendering each pixel and storing color, location and lighting data. GPUs have dedicated video memory and more cores than CPUs, making them better for processing large blocks of data. The Pascal GPU uses 16nm technology, HBM2 memory, NVLink interconnect and unified memory to improve performance for graphics and deep learning applications like physics simulations and image processing.
The document discusses the Intel Core i7 processor. It has the following key points:
1. The Core i7 is a quad-core desktop processor using the Intel Nehalem microarchitecture.
2. It uses the LGA1366 socket and supports DDR3 RAM via an on-die memory controller.
3. The front-side bus is replaced by the faster QuickPath Interconnect for communication with the chipset.
GPUs are dedicated parallel processors that are optimized for accelerating graphical computations. They have many execution units and faster memory interfaces than CPUs in order to process large amounts of graphical data efficiently. The GPU pipeline receives geometry from the CPU as input and provides pictures as output, going through stages like vertex processing, triangle setup, pixel processing, and output merging. GPUs are highly programmable and widely used for applications like gaming, shading, and global illumination. Future advancements may include more processing cores, tighter integration with CPUs, and fully programmable hardware.
1) The document discusses shortest path algorithms and their application to traffic assignment problems, comparing the performance of CPU vs GPU implementations.
2) It finds that GPU implementations can be 45x faster than CPU for problems with massive parallelizable data like traffic simulations.
3) However, GPU programming requires more specialized knowledge and hardware restrictions limit accessibility, while CPU remains more flexible but less optimized for large datasets.
The document provides an overview of graphics processing units (GPUs). It defines a GPU as a dedicated processor for computer graphics that contains hundreds of parallel execution units tailored for graphics processing. The document compares GPUs to CPUs, describing how GPUs have many parallel units while CPUs operate serially. It outlines the typical architecture of a GPU, including its pipeline from vertex processing to pixel processing to memory storage. The document also discusses how GPUs interact with CPUs and their use of dedicated video memory.
CPUs have multiple cores and are suited for a variety of tasks requiring low latency, while GPUs have many smaller cores optimized for parallel processing. GPUs can perform thousands of basic operations simultaneously. Though CPUs and GPUs both process data, CPUs are designed for serial tasks and general computing while GPUs are specialized for graphics processing and parallel tasks like those in AI and supercomputing. Efficient parallel processing is what allows GPUs to outperform CPUs for certain applications.
This document provides an overview of graphics cards, including:
- A brief history of graphics cards from the 1970s to modern PCs in 1995.
- Explanations that graphics cards are the GPU and contain dedicated RAM for graphics processing.
- The main manufacturers are Nvidia, AMD, and ATI and cards have heatsinks and fans for cooling.
- Having two graphics cards can prolong life through technologies like SLI and Crossfire by splitting the workload.
- Graphics cards allow for benefits like higher framerates, resolutions, and visual effects in games.
The document discusses different types of computer system organizations based on the number of general-purpose processors used: single-processor systems which use one main CPU, multiprocessor/multicore systems which contain two or more closely communicating processors, and clustered systems which gather multiple complete computer systems together. Single-processor systems may contain additional special-purpose processors like disk or keyboard controllers. Multiprocessor systems can be symmetric, with all processors performing all tasks, or asymmetric with dedicated tasks. Clustered systems provide high-availability and parallel processing across nodes.
Solid state drives use solid state memory like NAND flash instead of spinning disks. They have faster access times than hard disk drives. An SSD contains a controller, flash memory, and an interface. The controller manages read and write operations to the flash which is organized into pages and blocks. SSDs are found in devices like thumb drives, memory cards, and embedded systems. They provide benefits like faster startup, access, and application loading compared to HDDs. SSDs are used where fast storage access is important, like financial trading systems.
This document provides an overview of key concepts in digital image processing, including:
1. It discusses fundamental steps like image acquisition, enhancement, color image processing, and wavelets and multiresolution processing.
2. Image enhancement techniques process images to make them more suitable for specific applications.
3. Color image processing has increased in importance due to more digital images on the internet. Wavelets allow images to be represented at various resolution levels.
This document discusses parallel computing with GPUs. It introduces parallel computing, GPUs, and CUDA. It describes how GPUs are well-suited for data-parallel applications due to their large number of cores and throughput-oriented design. The CUDA programming model is also summarized, including how kernels are launched on the GPU from the CPU. Examples are provided of simple CUDA programs to perform operations like squaring elements in parallel on the GPU.
GPU computing provides a way to access the power of massively parallel graphics processing units (GPUs) for general purpose computing. GPUs contain over 100 processing cores and can achieve over 500 gigaflops of performance. The CUDA programming model allows programmers to leverage this parallelism by executing compute kernels on the GPU from their existing C/C++ applications. This approach democratizes parallel computing by making highly parallel systems accessible through inexpensive GPUs in personal computers and workstations. Researchers can now explore manycore architectures and parallel algorithms using GPUs as a platform.
This document provides an overview of multi-core processors, including their history, architecture, advantages, disadvantages, applications and future aspects. It discusses how multi-core processors work with multiple independent processor cores on a single chip to improve performance over single-core processors. Some key points covered include the introduction of dual-core chips by IBM, Intel and AMD in the early 2000s; comparisons of single-core, multi-core and other architectures; advantages like improved multi-tasking and security; and challenges for software to fully utilize multi-core capabilities.
Image classification with Deep Neural NetworksYogendra Tamang
This document discusses image classification using deep neural networks. It provides background on image classification and convolutional neural networks. The document outlines techniques like activation functions, pooling, dropout and data augmentation to prevent overfitting. It summarizes a paper on ImageNet classification using CNNs with multiple convolutional and fully connected layers. The paper achieved state-of-the-art results on ImageNet in 2010 and 2012 by training CNNs on a large dataset using multiple GPUs.
This document discusses different modes of data transfer between a central computer and input/output devices. It describes three main modes: programmed I/O, interrupt-initiated I/O, and direct memory access (DMA). Programmed I/O involves constant CPU monitoring of data transfers. Interrupt-initiated I/O uses interrupts to signal the CPU when a transfer is ready. DMA allows direct transfer between peripherals and memory without using the CPU. The document provides details on how each mode functions and when they may be used.
Holographic memory seminar ppt contains all aspects of holography and holographic storage. It provide history and technical background of holography. Contains reading and writing data into photopolymer. Lack of development of HDSS, its application and conclusion.
The CPU diagram shows the main components of a computer including the processor, storage, input and output devices, cache, and main memory. The input device sends instructions to the processor to retrieve applications from main memory, which are then displayed on the output device monitor. The cache acts as a buffer to temporarily store applications and processes for faster access. Processors have multiple cores that operate at gigahertz speeds to process instructions. The fetch-execute cycle continuously loops to fetch instructions from storage into memory, decode them, and execute the decoded operations. The arithmetic logic unit performs calculations and logical comparisons on input data from the control unit and outputs results.
Transform coding is used in image and video processing to exploit correlations between neighboring pixels. The discrete cosine transform (DCT) is commonly used as it provides good energy compaction and de-correlation. DCT represents data as a sum of variable frequency cosine waves in the frequency domain. It has properties like separability and orthogonality that make it efficient for computation. DCT exhibits excellent energy compaction and removal of redundancy between pixels, making it useful for image and video compression.
A graphics processing unit (GPU) is a microprocessor designed specifically to process graphics. It handles millions of math-intensive processes like 3D rendering per second. This allows real-time 3D graphics on PCs and game consoles that were previously only available on high-end workstations. A GPU takes the computationally intensive graphics tasks off the CPU to improve performance. It has integrated components like transform and lighting engines to handle 3D graphics processing more efficiently than a general-purpose CPU.
This document discusses GPU architecture and functioning. It defines a GPU as a processor optimized for graphics, video and visual computing. GPUs are highly parallel and multithreaded. The document outlines the evolution of GPUs from specialized graphics cards to massively parallel processors capable of general purpose computing. It describes the GPU pipeline including host interface, vertex processing, triangle setup, fragment processing and memory interface. It also discusses programming models like CUDA and differences between CPUs and GPUs.
Nvidia (History, GPU Architecture and New Pascal Architecture)Saksham Tanwar
A GPU is an electronic circuit that rapidly manipulates memory to accelerate image processing and display. Modern GPUs use parallel processing, rendering each pixel and storing color, location and lighting data. GPUs have dedicated video memory and more cores than CPUs, making them better for processing large blocks of data. The Pascal GPU uses 16nm technology, HBM2 memory, NVLink interconnect and unified memory to improve performance for graphics and deep learning applications like physics simulations and image processing.
The document discusses the Intel Core i7 processor. It has the following key points:
1. The Core i7 is a quad-core desktop processor using the Intel Nehalem microarchitecture.
2. It uses the LGA1366 socket and supports DDR3 RAM via an on-die memory controller.
3. The front-side bus is replaced by the faster QuickPath Interconnect for communication with the chipset.
GPUs are dedicated parallel processors that are optimized for accelerating graphical computations. They have many execution units and faster memory interfaces than CPUs in order to process large amounts of graphical data efficiently. The GPU pipeline receives geometry from the CPU as input and provides pictures as output, going through stages like vertex processing, triangle setup, pixel processing, and output merging. GPUs are highly programmable and widely used for applications like gaming, shading, and global illumination. Future advancements may include more processing cores, tighter integration with CPUs, and fully programmable hardware.
1) The document discusses shortest path algorithms and their application to traffic assignment problems, comparing the performance of CPU vs GPU implementations.
2) It finds that GPU implementations can be 45x faster than CPU for problems with massive parallelizable data like traffic simulations.
3) However, GPU programming requires more specialized knowledge and hardware restrictions limit accessibility, while CPU remains more flexible but less optimized for large datasets.
The document provides an overview of graphics processing units (GPUs). It defines a GPU as a dedicated processor for computer graphics that contains hundreds of parallel execution units tailored for graphics processing. The document compares GPUs to CPUs, describing how GPUs have many parallel units while CPUs operate serially. It outlines the typical architecture of a GPU, including its pipeline from vertex processing to pixel processing to memory storage. The document also discusses how GPUs interact with CPUs and their use of dedicated video memory.
CPUs have multiple cores and are suited for a variety of tasks requiring low latency, while GPUs have many smaller cores optimized for parallel processing. GPUs can perform thousands of basic operations simultaneously. Though CPUs and GPUs both process data, CPUs are designed for serial tasks and general computing while GPUs are specialized for graphics processing and parallel tasks like those in AI and supercomputing. Efficient parallel processing is what allows GPUs to outperform CPUs for certain applications.
This document provides an overview of graphics cards, including:
- A brief history of graphics cards from the 1970s to modern PCs in 1995.
- Explanations that graphics cards are the GPU and contain dedicated RAM for graphics processing.
- The main manufacturers are Nvidia, AMD, and ATI and cards have heatsinks and fans for cooling.
- Having two graphics cards can prolong life through technologies like SLI and Crossfire by splitting the workload.
- Graphics cards allow for benefits like higher framerates, resolutions, and visual effects in games.
The document discusses different types of computer system organizations based on the number of general-purpose processors used: single-processor systems which use one main CPU, multiprocessor/multicore systems which contain two or more closely communicating processors, and clustered systems which gather multiple complete computer systems together. Single-processor systems may contain additional special-purpose processors like disk or keyboard controllers. Multiprocessor systems can be symmetric, with all processors performing all tasks, or asymmetric with dedicated tasks. Clustered systems provide high-availability and parallel processing across nodes.
Solid state drives use solid state memory like NAND flash instead of spinning disks. They have faster access times than hard disk drives. An SSD contains a controller, flash memory, and an interface. The controller manages read and write operations to the flash which is organized into pages and blocks. SSDs are found in devices like thumb drives, memory cards, and embedded systems. They provide benefits like faster startup, access, and application loading compared to HDDs. SSDs are used where fast storage access is important, like financial trading systems.
This document provides an overview of key concepts in digital image processing, including:
1. It discusses fundamental steps like image acquisition, enhancement, color image processing, and wavelets and multiresolution processing.
2. Image enhancement techniques process images to make them more suitable for specific applications.
3. Color image processing has increased in importance due to more digital images on the internet. Wavelets allow images to be represented at various resolution levels.
This document discusses parallel computing with GPUs. It introduces parallel computing, GPUs, and CUDA. It describes how GPUs are well-suited for data-parallel applications due to their large number of cores and throughput-oriented design. The CUDA programming model is also summarized, including how kernels are launched on the GPU from the CPU. Examples are provided of simple CUDA programs to perform operations like squaring elements in parallel on the GPU.
GPU computing provides a way to access the power of massively parallel graphics processing units (GPUs) for general purpose computing. GPUs contain over 100 processing cores and can achieve over 500 gigaflops of performance. The CUDA programming model allows programmers to leverage this parallelism by executing compute kernels on the GPU from their existing C/C++ applications. This approach democratizes parallel computing by making highly parallel systems accessible through inexpensive GPUs in personal computers and workstations. Researchers can now explore manycore architectures and parallel algorithms using GPUs as a platform.
This document provides an overview of multi-core processors, including their history, architecture, advantages, disadvantages, applications and future aspects. It discusses how multi-core processors work with multiple independent processor cores on a single chip to improve performance over single-core processors. Some key points covered include the introduction of dual-core chips by IBM, Intel and AMD in the early 2000s; comparisons of single-core, multi-core and other architectures; advantages like improved multi-tasking and security; and challenges for software to fully utilize multi-core capabilities.
Image classification with Deep Neural NetworksYogendra Tamang
This document discusses image classification using deep neural networks. It provides background on image classification and convolutional neural networks. The document outlines techniques like activation functions, pooling, dropout and data augmentation to prevent overfitting. It summarizes a paper on ImageNet classification using CNNs with multiple convolutional and fully connected layers. The paper achieved state-of-the-art results on ImageNet in 2010 and 2012 by training CNNs on a large dataset using multiple GPUs.
This document discusses different modes of data transfer between a central computer and input/output devices. It describes three main modes: programmed I/O, interrupt-initiated I/O, and direct memory access (DMA). Programmed I/O involves constant CPU monitoring of data transfers. Interrupt-initiated I/O uses interrupts to signal the CPU when a transfer is ready. DMA allows direct transfer between peripherals and memory without using the CPU. The document provides details on how each mode functions and when they may be used.
Holographic memory seminar ppt contains all aspects of holography and holographic storage. It provide history and technical background of holography. Contains reading and writing data into photopolymer. Lack of development of HDSS, its application and conclusion.
The CPU diagram shows the main components of a computer including the processor, storage, input and output devices, cache, and main memory. The input device sends instructions to the processor to retrieve applications from main memory, which are then displayed on the output device monitor. The cache acts as a buffer to temporarily store applications and processes for faster access. Processors have multiple cores that operate at gigahertz speeds to process instructions. The fetch-execute cycle continuously loops to fetch instructions from storage into memory, decode them, and execute the decoded operations. The arithmetic logic unit performs calculations and logical comparisons on input data from the control unit and outputs results.
Transform coding is used in image and video processing to exploit correlations between neighboring pixels. The discrete cosine transform (DCT) is commonly used as it provides good energy compaction and de-correlation. DCT represents data as a sum of variable frequency cosine waves in the frequency domain. It has properties like separability and orthogonality that make it efficient for computation. DCT exhibits excellent energy compaction and removal of redundancy between pixels, making it useful for image and video compression.
A graphics processing unit (GPU) is a microprocessor designed specifically to process graphics. It handles millions of math-intensive processes like 3D rendering per second. This allows real-time 3D graphics on PCs and game consoles that were previously only available on high-end workstations. A GPU takes the computationally intensive graphics tasks off the CPU to improve performance. It has integrated components like transform and lighting engines to handle 3D graphics processing more efficiently than a general-purpose CPU.
The document discusses NVIDIA graphics hardware over seven years, the Cg programming language, and transparency techniques. It describes the evolution of NVIDIA GPUs and features like GeForce cards, increased processing power, and support for DirectX. It promotes Cg as a cross-platform language for GPU programming. It also explains the depth peeling algorithm for rendering transparency in real-time using multiple rendering passes.
The document compares the hardware architectures and graphics processing capabilities of the Xbox 360 and PlayStation 3 game consoles. Both consoles use procedural synthesis to optimize rendering of 3D scenes from stored data. The Xbox 360 uses a GPU designed specifically for gaming by Microsoft and ATI, while the PS3 uses a GPU not primarily designed for gaming. As a result, the Xbox 360's graphics rendering is generally more optimized for games. However, both consoles have trade-offs, so the best choice depends on intended use and price.
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Computer Architecture and Organization
V semester
Anna University
By
Babu M, Assistant Professor
Department of ECE
RMK College of Engineering and Technology
Chennai
This document discusses computer graphics and graphics workstations. It provides details on:
- The capabilities of typical graphics workstations, including 2D/3D modeling, animation, color selection, 3D viewing, and other features.
- The architecture of graphics workstations, which typically include a RISC microprocessor, large memory and storage, and specialized graphics boards.
- Common display algorithms used in computer graphics like raster scan techniques, DDA and Bresenham's line drawing algorithms, which allow for fast rendering of basic shapes and lines on raster displays.
The document discusses visualization systems and proposes concepts for their future development. It summarizes:
1) The "Visual Realityware" visualization software development environment, which uses an abstraction layer to allow developers to freely select mainstream graphics technologies and expand applications across multiple platforms with minimal bugs.
2) An application called "Virtual Anatomia" developed using Visual Realityware to visualize 3D biological data in real-time.
3) The concept of "Visionize" which is defined as a risk management methodology using visual communication to allow sharing of goals and visions in order to identify and prevent risks before issues arise.
GPUs are specialized processors designed for graphics processing. CUDA (Compute Unified Device Architecture) allows general purpose programming on NVIDIA GPUs. CUDA programs launch kernels across a grid of blocks, with each block containing multiple threads that can cooperate. Threads have unique IDs and can access different memory types including shared, global, and constant memory. Applications that map well to this architecture include physics simulations, image processing, and other data-parallel workloads. The future of CUDA includes more general purpose uses through GPGPU and improvements in virtual memory, size, and cooling.
Monte Carlo simulation is well-suited for GPU acceleration due to its highly parallel nature. GPUs provide lower cost and higher performance than CPUs for Monte Carlo applications. Numerical libraries for GPUs allow developers to focus on their models rather than reimplementing basic components. NAG has developed GPU libraries including random number generators and is working with financial institutions to apply Monte Carlo simulations to problems in finance.
The document discusses VPU and GPGPU computing. It explains that a VPU is a visual processing unit, also known as a GPU. GPUs are massively parallel and multithreaded processors that are better than CPUs for tasks like machine learning and graphics processing. The document then discusses GPU architecture, memory, and programming models like CUDA. It provides examples of GPU usage and concludes that GPGPU is used in fields like machine learning, robotics, and scientific computing.
This document summarizes VPU and GPGPU computing technologies. It discusses that a VPU is a visual processing unit, also known as a GPU. GPUs have massively parallel architectures that allow them to perform better than CPUs for some complex computational tasks. The document then discusses GPU, PPU and GPGPU architectures, programming models like CUDA, and applications of GPGPU computing such as machine learning, robotics and scientific research.
This document summarizes VPU and GPGPU technologies. It discusses that a VPU is a visual processing unit, also known as a GPU. GPUs have massively parallel architectures that allow them to perform better than CPUs for some complex computational tasks. The document then discusses GPU architecture including stream processing, graphics pipelines, shaders, and GPU clusters. It provides an example of using CUDA for GPU computing and discusses how GPUs are used for general purpose computing through frameworks like CUDA.
This document summarizes VPU and GPGPU computing technologies. It discusses that a VPU is a visual processing unit, also known as a GPU. GPUs provide massively parallel and multithreaded processing capabilities. GPUs are now commonly used for general purpose computing due to their ability to handle complex computational tasks faster than CPUs in some cases. The document then discusses GPU and PPU architectures, programming models like CUDA, and applications of GPGPU computing such as machine learning, robotics, and scientific research.
Parallel Graphics in Frostbite - Current & Future (Siggraph 2009)Johan Andersson
1. The document discusses parallel graphics techniques used in the Frostbite game engine, both currently and potentially in the future. It describes using job-based parallelism to utilize multiple CPU cores and the PS3 SPUs.
2. One technique is parallel command buffer recording to dispatch draw calls to multiple command buffers and scale linearly with core count. Another is software occlusion culling using the SPUs/CPU to rasterize a coarse z-buffer.
3. Potential future techniques discussed include deferred shading using compute shaders, with the compute shader culling lights and accumulating lighting per screen-space tile.
The document discusses NVIDIA data center GPUs such as the A100, A30, A40, and A10 and their performance capabilities. It provides examples of GPU accelerated application performance showing simulations in Simulia CST Studio, Altair CFD, and Rocky DEM achieving excellent speedups on GPUs. It also discusses Paraview visualization being accelerated with NVIDIA OptiX ray tracing, further sped up using RT cores. Looking ahead, the document outlines NVIDIA Grace CPUs which are designed to improve memory bandwidth between CPUs and GPUs for giant AI and HPC models.
This document discusses the implementation of a finite impulse response (FIR) filter on a graphics processing unit (GPU). It outlines how FIR filters can be represented using textures on the GPU and implemented using fragment programs. The performance of FIR filters and related transformations implemented on the GPU is evaluated. Texture upload and download between GPU and main memory accounts for up to 60% of the total processing time. While GPU computation is faster than CPU for these algorithms, optimization techniques from CPU programming do not always apply to the GPU.
The GeForce FX was Nvidia's fifth generation GeForce graphics card, featuring support for Shader Model 2.0. It aimed to improve on previous GeForce cards with faster core and memory speeds of 500MHz and 250MHz respectively, new anisotropic filtering and video processing capabilities. However, it ended up underperforming compared to AMD/ATI's competing Radeon 9700 and 9800 cards due to its narrower 128-bit memory bus and issues with its mixed-precision shader architecture. This disappointed hardware experts and consumers expecting greater performance.
Nowadays modern computer GPU (Graphic Processing Unit) became widely used to improve the
performance of a computer, which is basically for the GPU graphics calculations, are now used not only
for the purposes of calculating the graphics but also for other application. In addition, Graphics
Processing Unit (GPU) has high computation and low price. This device can be treat as an array of SIMD
processor using CUDA software. This paper talks about GPU application, CUDA memory and efficient
CUDA memory using Reduction kernel. High-performance GPU application requires reuse of data inside
the streaming multiprocessor (SM). The reason is that onboard global memory is simply not fast enough to
meet the needs of all the streaming multiprocessor on the GPU. In addition, CUDA exposes the memory
space within the SM and provides configurable caches to give the developer the greatest opportunity of
data reuse.
The document discusses HTML, including its definition as a markup language used to create web pages, its purpose to tell browsers how to display web page elements, and the requirements and basic implementation of HTML using tags. It also lists different versions of HTML and references for learning more.
Machine learning ppt
college presentation on Machine Learning Programming releated them. explain each and every Point in detail so. thats why they are easily to explain in the
Seminar topic on holography, they are used for final year student or 3rd year student to get selection of topic on seminar and explain in front of collage students
This document contains descriptions of several code optimization practicals:
1. It describes taking an input string, generating three-address intermediate code, and then optimizing the code by combining operations like multiplication and addition wherever possible.
2. It provides an example input and output showing the original three-address code and optimized code.
3. The code optimization involves identifying operators like * and + and generating temporary variables to store sub-expressions, combining operations wherever adjacent operations use the same operands.
Python lab manual all the experiments are availableNitesh Dubey
The document describes 10 experiments related to Python programming. Each experiment has an aim to write a Python program to perform a specific task like finding the GCD of two numbers, calculating square root using Newton's method, exponentiation of a number, finding the maximum of a list, performing linear search, binary search, selection sort, insertion sort, merge sort, and multiplying matrices. For each experiment, the algorithm and Python program to implement it is provided. The output for sample test cases is also given to verify the programs.
Web Technology Lab files with practicalNitesh Dubey
The document describes several experiments using HTML, CSS, JavaScript, Java, and SQL to develop web applications.
Experiment 1 involves creating a CV using HTML and JavaScript and displaying it on different websites. Experiment 2 creates a student details form in HTML that sends data to a database.
Experiment 3 uses JavaScript to display browser information on a web page. Experiment 4 develops a calculator application using JavaScript.
Experiment 5 defines document type definitions and cascading style sheets to style an XML document about books.
Experiment 6 connects to a database using JDBC and SQL. It retrieves and updates data, designing a simple servlet to query a book database.
Theory of automata and formal language lab manualNitesh Dubey
The document describes several experiments related to compiler design including lexical analysis, parsing, and code generation.
Experiment 1 involves writing a program to identify if a given string is an identifier or not using a DFA. Experiment 2 simulates a DFA to check if a string is accepted by the given automaton. Experiment 3 checks if a string belongs to a given grammar using a top-down parsing approach. Experiment 4 implements recursive descent parsing to parse expressions based on a grammar. Experiment 5 computes FIRST and FOLLOW sets and builds a LL(1) parsing table for a given grammar. Experiment 6 implements shift-reduce parsing to parse strings. Experiment 7 generates intermediate code like Polish notation, 3-address code, and quadruples
Here are the steps to develop a UML use case diagram for the given problem:
1. Identify the system and actors
The system is the "Supermarket Loyalty Program". The actors are "Customer" and "Supermarket Staff".
2. Identify the use cases
The key use cases are:
- Register for Loyalty Program
- Make Purchase
- View Purchase History
- Generate Prize Winners List
- Reset Purchase Entries
3. Draw and label the use case diagram
Draw oval shapes for the use cases and stick figures for the actors. Connect the actors to related use cases with lines. Label all elements.
4. Add descriptions to use cases
Principal of programming language lab files Nitesh Dubey
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help alleviate symptoms of mental illness and boost overall mental well-being.
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can help calm the mind and body by lowering heart rate and blood pressure. Making meditation a part of a daily routine, even if just 10-15 minutes per day, can offer improvements to mood, focus, and overall well-being over time.
design and analysis of algorithm Lab filesNitesh Dubey
This document contains details of experiments conducted as part of a "Design and Analysis of Algorithm Lab" course. It includes 10 experiments covering algorithms like binary search, heap sort, merge sort, selection sort, insertion sort, quick sort, knapsack problem, travelling salesman problem, minimum spanning tree (using Kruskal's algorithm), and N queen problem (using backtracking). For each experiment, it provides the objective, program code implementation, and result. The document is submitted by a student to their professor for the lab session.
Computer Organization And Architecture lab manualNitesh Dubey
The document discusses the implementation of various logic gates and flip-flops. It describes half adders and full adders can be implemented using XOR and AND gates. Binary to gray code and gray to binary code conversions are also explained. Circuit diagrams for 3-8 line decoder, 4x1 and 8x1 multiplexer are provided along with their truth tables. Finally, the working of common flip-flops like SR, JK, D and T are explained through their excitation tables.
industrial training report on Ethical hackingNitesh Dubey
This document outlines an industrial training report on ethical hacking conducted at Alison Online Training Institute. It begins with an introduction to ethical hacking and the different types of hacking. It then discusses the role of security and penetration testers and different penetration testing methodologies. The document provides an overview of what can and cannot be done legally as an ethical hacker. It also discusses the basics of networking and what it takes to be a successful security tester.
Project synopsis on face recognition in e attendanceNitesh Dubey
This document provides a project synopsis for a face recognition-based e-attendance system. It discusses developing an automated attendance system using face recognition technology to address issues with traditional manual attendance methods, such as being time-consuming and allowing for fraudulent attendance. The objectives are to help teachers track and manage student attendance and absenteeism more efficiently. The proposed system uses face detection and recognition algorithms to automatically mark student attendance based on detecting faces in the classroom. It includes modules for image capture, face detection, preprocessing, database development, and postprocessing for recognition. Feasibility analysis indicates the technical feasibility of the system using existing technologies. Methodology diagrams show the training and recognition workflows that involve face detection, feature extraction, and classification.
This document provides an overview of the system analysis conducted for developing a Human Resource Management System (HRMS) for BittCell Systems Pvt. Ltd. Key aspects of the analysis included collecting requirements, studying the current manual system, identifying needs and limitations, and conducting a feasibility study. Tools used in the analysis included data collection, charting, dictionaries, and ER diagrams to understand information flow and relationships. The proposed HRMS aims to increase efficiency by automating employee registration, leave management, payroll, and training processes.
Industrial training report on core java Nitesh Dubey
This document discusses the installation and configuration of Java. It begins with an overview of Java and its key features like platform independence. It then discusses the Java platform and how bytecode is run by the Java Virtual Machine (JVM) across different operating systems. The document also covers installing Java, configuring variables, writing and running a basic Java program, and some Java concepts like packages, classes, objects, and modifiers.
SEWAGE TREATMENT PLANT mini project reportNitesh Dubey
This document provides information about a research project analyzing the quality of treated sewage water from shipboard sewage treatment plants. Water samples were taken from 32 ships and analyzed for parameters like coliform bacteria, suspended solids, and biological oxygen demand. The results showed that none of the treated sewage water samples met standards in the MARPOL Annex IV regulations. The document also describes regulations for sewage discharge, potential health and environmental risks of untreated sewage, and common types of sewage treatment systems used on ships.
synopsis report on BIOMETRIC ONLINE VOTING SYSTEMNitesh Dubey
The document summarizes the design of a biometric-based online voting system. It discusses including voter secrecy, authentication, vote verification and accuracy. The design goals are to safely transfer votes from the user's computer to the server and securely store cast votes. The system will use fingerprint biometrics for voter verification and only allow each verified voter to cast one vote. It will also provide manuals for voters before the election and allow vote verification before finalizing.
A.I. refers to the capability of machines to imitate intelligent human behavior. The history of A.I. began in the 1950s but has improved greatly in recent decades with advances like Sophia robot. A.I. is needed because humans have physical limitations, while robots can perform dangerous jobs. A.I. is created through a combination of programming, hardware, and sensors. It has many applications like healthcare, education, industry, finance, and customer support. While A.I. provides benefits like low error rates and replacing humans in dangerous jobs, there are also disadvantages such as high costs, lack of creativity, and potential unemployment. The future of A.I. could include automated transportation, cyborg technology
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
Better Builder Magazine brings together premium product manufactures and leading builders to create better differentiated homes and buildings that use less energy, save water and reduce our impact on the environment. The magazine is published four times a year.
Cricket management system ptoject report.pdfKamal Acharya
The aim of this project is to provide the complete information of the National and
International statistics. The information is available country wise and player wise. By
entering the data of eachmatch, we can get all type of reports instantly, which will be
useful to call back history of each player. Also the team performance in each match can
be obtained. We can get a report on number of matches, wins and lost.
Covid Management System Project Report.pdfKamal Acharya
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
3. Introduction
A Graphics Processing Unit (GPU) is a microprocessor that
has been designed specifically for the processing of 3D
graphics.
The processor is built with integrated transform, lighting,
triangle setup/clipping, and rendering engines, capable of
handling millions of math-intensive processes per second.
GPUs form the heart of modern graphics cards, relieving
the CPU (central processing units) of much of the graphics
processing load.
4. Why GPU?
To provide a separate dedicated graphics
resources including a graphics processor and
memory.
To relieve some of the burden of the main system
resources, namely the Central Processing Unit,
Main Memory, and the System Bus, which would
otherwise get saturated with graphical operations
and I/O requests.
5. What is a GPU?
A Graphics Processing Unit or GPU (also
occasionally called Visual Processing Unit or
VPU) is a dedicated processor efficient at
manipulating and displaying computer graphics .
Like the CPU (Central Processing Unit), it is a
single-chip processor.
6. HOWEVER,
The abstract goal of a GPU, is to enable a
representation of a 3D world as realistically as
possible. So these GPUs are designed to provide
additional computational power that is customized
specifically to perform these 3D tasks.
7. GPU vs CPU
A GPU is tailored for highly parallel operation
while a CPU executes programs serially.
For this reason, GPUs have many parallel
execution units , while CPUs have few execution
units .
GPUs have singificantly faster and more
advanced memory interfaces as they need to shift
around a lot more data than CPUs.
GPUs have much deeper pipelines (several
thousand stages vs 10-20 for CPUs).
10. GPU Architecture
How many processing units?
Lots.
How many ALUs?
Hundreds.
Do you need a cache?
Sort of.
What kind of memory?
very fast.
12. The GPU pipeline
The GPU receives geometry information from the
CPU as an input and provides a picture as an
output
Let’s see how that happens…
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
13. Host Interface
The host interface is the communication bridge
between the CPU and the GPU.
It receives commands from the CPU and also
pulls geometry information from system memory.
It outputs a stream of vertices in object space
with all their associated information (texture
coordinates, per vertex color etc) .
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
14. Vertex Processing
The vertex processing stage receives vertices from the
host interface in object space and outputs them in screen
space
This may be a simple linear transformation, or a complex
operation involving morphing effects
No new vertices are created in this stage, and no vertices
are discarded (input/output has 1:1 mapping)
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
15. Triangle setup
In this stage geometry information becomes
raster information (screen space geometry is the
input, pixels are the output)
Prior to rasterization, triangles that are
backfacing or are located outside the viewing
frustrum are rejected
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
16. Triangle Setup (cont…..)
A pixel is generated if and only if its center is inside
the triangle
Every pixel generated has its attributes computed to
be the perspective correct interpolation of the three
vertices that make up the triangle
17. Pixel Processing
Each pixel provided by triangle setup is fed into
pixel processing as a set of attributes which are
used to compute the final color for this pixel
The computations taking place here include
texture mapping and math operations
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
18. Memory Interface
Pixel colors provided by the previous stage are written to
the framebuffer
Used to be the biggest bottleneck before pixel processing
took over
Before the final write occurs, some pixels are rejected by
the zbuffer .On modern GPUs z is compressed to reduce
framebuffer bandwidth (but not size).
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
19. Programmability in GPU pipeline
In current state of the art GPUs, vertex and pixel
processing are now programmable
The programmer can write programs that are executed
for every vertex as well as for every pixel
This allows fully customizable geometry and shading
effects that go well beyond the generic look and feel of
older 3D applications
host
interface
vertex
processing
triangle
setup
pixel
processing
memory
interface
20. Conclusion
Graphics Processing Unit is not a wonder that this
piece of hardware is often referred to as an exotic
product as far as computer peripherals are
concerned.
By observing the current pace at which work is going
on in developing GPUs we can surely come to a
conclusion that we will be able to see better and
faster GPUs in the near future.
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