# Multidimensional grids and shared memory for cuda python with Numba ### Content covered * utiliizng the on chip memory space on gpu called shared memory ### Outcome: * writing the GPU accelerated code in python using numba on 1D and 2D datasets while utilizing the several of the most important strategies for writing consistently fast GPU accelerated code. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://somesh.gitbook.io/somesh-fengade/notes-and-courses/introduction-to-cuda-python-with-numba/multidimensional-grids-and-shared-memory-for-cuda-python-with-numba.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.