Python has become a pervasive and useful tool in advancing scientific research and computation. Python has a very rich ecosystem of open source packages for mathematics, science, and engineering, anchored on the performant numerical computation on arrays and matrices, data analysis and visualization capabilities, and an interactive development environment that enables rapid and collaborative iteration of ideas. Python is used to discover new objects in space, calculate thermodynamics, conduct genomic analysis of cancer, estimate the likehood of earthquakes, simulate musculoskeletal systems, visualize astroid chemistries, and much more.
Intel®’s accelerated Python packages enable scientists to take advantage of the productivity of Python, while taking advantage of the ever-increasing performance of modern hardware. Intel®’s optimized implementations of NumPy and SciPy leverage the Intel® oneAPI Math Kernel Library to achieve highly efficient multi-threading, vectorization, and memory management. Intel®’s optimized implementation of mpi4py leverages the Intel®MPI Library to scale scientific computation efficiently across a cluster. These are all drop-in performance enhancements to existing Python code to enable faster processing of larger scientific data sets – to enable new discoveries on billions of galaxies or billions of atoms.