Hosted by: Chi-kwan Chan
Description: Python is a favorite for data science and scientific computing, but its interpreted nature can be a performance bottleneck. In this workshop, we will explore practical techniques to supercharge Python performance, including vectorization, parallel/distributed computing, just-in-time compilation, and GPU acceleration. We will also cover profiling best practices to benchmark and optimize codes. Whether you’re processing large datasets, training AI models, or running simulations, these strategies will help you achieve speedups of up to 1000x.
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