# AMD-Instinct-GPU-Programming-Notes [`This SITE is under construction`]\ Major use cases where AMD Instinct GPU is considered are AI/ML compute and HPC compute. Getting up to speed with AMD Instict GPU could be quite challenging to the uninitiated without who don't have backgrounds on GPU compute, AI and HPC use cases. In order to ramp up AMD Instict GPU quickly, I have explored various topics so that programming AMD Instict GPU becomes easier and I understand the bigger picture, as to why to use the Instict GPU, how it can help a certain use case and so on. First, I try to quickly ramp up on Cloud computing and HPC computing. For Cloud computing the subfolder has all the details about the resources that I used to feel comfortable running AI workload in the Google Cloud for example. I explore HPC computing as well. This exploration was relatively easier as I am comfortable with Linux from the standpoint of using command lines etc. Once the use-cases are understood, I then explored the parallel programming languages for GPUs like CUDA, OpenCL and HIP, HIP being AMD's GPU programming languages. For each of this subject I have separate folders where I documented all the resources that I have used to get up to speed. Finally, I dived into the ROCm ecosystem of AMD. The knowledge of ROCm is a MUST when you are dealing with AMD Instict GPU.

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