Summary of Nvidia CUDA in 100 Seconds

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  CUDA is a parallel computing platform created by Nvidia in 2007, based on the work of Ian Buck and John Nichols. It allows the GPU to be used for tasks beyond gaming by enabling large-scale data computation. GPUs excel at matrix multiplication and vector transformations in parallel and modern GPUs have thousands of cores for such operations. With CUDA, developers can harness the power of the GPU to train machine learning models by writing CUDA kernels that run parallelly on the GPU. The process involves transferring data from main memory to GPU memory, executing the CUDA kernel in parallel, and transferring the results back to main memory. To build a CUDA application, you need an Nvidia GPU and the CUDA toolkit, typically coding in C++. The CUDA kernel performs operations on the GPU in parallel, with managed memory accessible by both the CPU and GPU. A main function initiates and runs the CUDA kernel on arrays of data, utilizing loops for data initialization and passing to the CUDA function.

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  The triple brackets in CUDA allow us to configure the kernel launch for parallel processing with specific blocks and threads. After synchronization, the data is copied back to the host machine for further use. To delve deeper into CUDA, attend NVIDIA's GTC conference for talks on building parallel systems. Thank you for watching!