[ FreeCourseWeb ] Udemy - Writing CUDA kernels for interpolation

[ FreeCourseWeb.com ] Writing CUDA kernels for interpolation

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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 6 lectures (1h 12m) | Size: 281.3 MB
Using CUDA textures to interpolate images
What you'll learn:
Nearest-neighbor interpolation of a PGM image
Writing CUDA kernels
Texture filtering
Bilinear interpolation of a PGM image
Texture loopkup
Bicubic interpolation of a PGM image
Interpolation in CUDA

Requirements
Fundamentals of C/C++ and CUDA programming
Basic elements of calculus, especially function approximation

Description
In real-life applications, we want big images: when we watch a video clip on a PC, we like to see it in the full-screen mode. We want high-quality images: if a block of pixels gets damaged during the transmission, we want to repair it. We want cool images: by digital image manipulation, fancy artistic effects as seen in movies can be rendered. We want fast processing, especially when the images are big and many. To process even faster, we want that the various image pixels are processed in parallel.

CUDA (Compute Unified Device Architecture) is a hardware architecture and programming model introduced by NVIDIA for the parallel processing of Graphics Processing Units (GPUs). It represents by now an assessed tool for parallel programming and permits low-level programming capable of achieving very high performance by directly and properly managing the thread work.

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