Nvidia (NVDA) said that with the NVIDIA cuPyNumeric accelerated computing library, researchers can now take their data-crunching Python code and effortlessly run it on CPU-based laptops and GPU-accelerated workstations, cloud servers or massive supercomputers. The faster they can work through their data, the quicker they can make decisions about promising data points, trends worth investigating and adjustments to their experiments. “To make the leap to accelerated computing, researchers don’t need expertise in computer science,” the company said. “They can simply write code using the familiar NumPy interface or apply cuPyNumeric to existing code, following best practices for performance and scalability. Once cuPyNumeric is applied, they can run their code on one or thousands of GPUs with zero code changes.”
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