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HomeHomework Helpcomputer-scienceCUDA Programming for GPUs

CUDA Programming for GPUs

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by Nvidia, allowing developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach known as GPGPU (General-Purpose computing on Graphics Processing Units).

intermediate
10 hours
Computer Science
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Overview

CUDA programming is a powerful tool that allows developers to harness the parallel processing capabilities of NVIDIA GPUs. By writing kernel functions that can be executed by thousands of threads simultaneously, CUDA enables significant performance improvements for a variety of applications, from sc...

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Key Terms

CUDA
A parallel computing platform and application programming interface model created by NVIDIA.

Example: Using CUDA, developers can harness the power of NVIDIA GPUs for complex computations.

Kernel
A function that runs on the GPU and is executed by multiple threads in parallel.

Example: In CUDA, a kernel is launched to perform operations on large datasets.

Thread
The smallest unit of processing that can be scheduled by an operating system.

Example: CUDA allows thousands of threads to run concurrently on a GPU.

Global Memory
A type of memory accessible by all threads in a CUDA program, but with higher latency.

Example: Data stored in global memory can be accessed by any thread during execution.

Shared Memory
A faster type of memory shared among threads in the same block.

Example: Using shared memory can significantly speed up data access in CUDA programs.

Occupancy
The ratio of active warps to the maximum number of warps supported on a multiprocessor.

Example: High occupancy can lead to better performance in CUDA applications.

Related Topics

OpenCL Programming
A framework for writing programs that execute across heterogeneous platforms, including GPUs.
intermediate
Parallel Algorithms
Algorithms designed to run on multiple processors simultaneously, improving efficiency.
advanced
Machine Learning with GPUs
Using GPU acceleration to enhance machine learning model training and inference.
intermediate
Computer Vision
Techniques for enabling computers to interpret and understand visual information from the world.
advanced

Key Concepts

Parallel ComputingKernel FunctionsMemory ManagementThread Hierarchy