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VOOZH | about |
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VOOZH | about |
AI Technical Writer
In 1996, NVIDIA entered the 3D accelerator market initially behind the competition. However, through constant learning and improvement, they achieved major success in 1999 with the introduction of the GeForce 256, recognized as the first graphics card termed a GPU. Initially designed for gaming, GPUs later found a plethora of business applications in math, science, and engineering.
In 2003, Ian Buck and his team introduced Brook, the initial widely embraced programming model that expanded C by incorporating data-parallel constructs. Buck later played a key role at NVIDIA, leading the 2006 launch of CUDA, the first commercially available solution for general-purpose computing on GPUs.
CUDA serves as the connecting bridge between Nvidia GPUs and GPU-based applications, enabling popular deep learning libraries like TensorFlow and PyTorch to leverage GPU acceleration. This capability is crucial for optimizing deep learning tasks and underscores the importance of using GPUs in the field. Today, CUDA is widely considered essential for any AI development, and is a software component of any AI development pipeline.
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With a strong background in data science and over six years of experience, I am passionate about creating in-depth content on technologies. Currently focused on AI, machine learning, and GPU computing, working on topics ranging from deep learning frameworks to optimizing GPU-based workloads.
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One correction: the GPU timing shows a roughly constant solve time even for huge inputs since nothing in the given code forces it to finish before the main program continues.
add a synchronize immediately after the torch.div command for GPU:
result_gpu = torch.div(x_gpu,y_gpu)
torch.cuda.synchronize() # forces GPU to finish before continuing
On my machine, this shows the CPU runtime scaling with n^2.2, and GPU with n^1.84
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