GPU Acceleration Module Tutorials

[NsansoneKx]

We want to convert the GPU module examples from https://gitlab.com/kxdev/kxinsights/data-science/data-lab/gpu/gpulib/-/blob/main/examples into explicitly runnable tutorials via Jupyter notebooks.

This MR achieves the above by adding the following:

• new GPU directory under tutorials/KDB-X/Modules that contains all required files for running these notebooks
• Dockerfile; builds the docker image that these notebooks can be run on (note this is required as the GPU module requires a specific enviornment configuration in order to run. More on this can be found here: https://kxdev.gitlab.io/-/documentation/docs-next/-/jobs/13580876958/artifacts/public/modules/gpu/quickstart/gpu-env.html)
• README.md; describes what the GPU Acceleration edition is, what it provides, links to each of the tutorial notebooks, Prerequisites for running a KDB-X GPU Application, and how to build/run the docker image and Jupyter notebooks
• asOfJoins.ipynb; Jupyter notebook for running the asOf Joins tutorial
• Sorting.ipynb; Jupyter notebook for running the tutorial for in-memory/on-disk sorting
• docs/hdbDataGen.md; description of what the genHDB.sh script does and how to use it for downloading a customizable amount of historical data/writing this to the HDB
• src/common.sh, src/genHDB.sh, gentab.q, tq.q; scripts for generating sample data that is utilized in the tutorials for showcasing GPU module optimizations