[1] Times by cuda https://developer.nvidia.com/blog/how-implement-performance-metrics-cuda-cc/
[2] GPU Code https://http.download.nvidia.com/developer/GPU_Gems/CD_Image/Index.html 
[3] Fluid in GPU theory https://developer.nvidia.com/gpugems/gpugems/part-vi-beyond-triangles/chapter-38-fast-fluid-dynamics-simulation-gpu
[4] GPU Computing 
https://researchcomputing.princeton.edu/support/knowledge-base/gpu-computing Cards [1] Guide https://www.nvidia.com/content/geforce-gtx/GEFORCE_RTX_2070_SUPER_User_Guide.pdf
[2] RTX 2070 Techinal summary  https://www.techpowerup.com/gpu-specs/geforce-rtx-2070-super.c3440
Important notes
* CUDA technology is 
developed by NVIDIA, and it is primarily designed to work with NVIDIA 
GPUs. CUDA is a parallel computing platform and application programming 
interface model that allows developers to use NVIDIA GPUs for 
general-purpose processing.
AMD has its own equivalent technology 
called OpenCL (Open Computing Language), which is an open standard for 
parallel programming of heterogeneous systems. OpenCL is not specific to
 AMD; it is supported by various vendors, including AMD, Intel, and 
others.
If you have an AMD GPU and want to leverage its parallel 
processing capabilities, you would typically use OpenCL rather than 
CUDA. However, it's important to note that not all software applications
 support both CUDA and OpenCL. The level of support depends on how the 
software was developed.
 * CUDA technology is specific to NVIDIA GPUs (Graphics Processing 
Units), and it cannot be used with AMD GPUs. CUDA (Compute Unified 
Device Architecture) is a parallel computing platform and programming 
model developed by NVIDIA for their GPUs. It includes a programming 
interface and software development kit (SDK) for general-purpose 
computing on NVIDIA GPUs.
On the other hand, AMD uses a different 
technology called OpenCL (Open Computing Language) for parallel 
computing across CPUs, GPUs, and other processors. OpenCL is an open 
standard maintained by the Khronos Group, and it is designed to be 
vendor-neutral, allowing developers to write code that can run on 
different hardware platforms, including AMD GPUs. For getting 
information about CAD Drafting and designing visit CAD Drafter.
If
 you have an AMD GPU and you are looking to perform GPU-accelerated 
computing, you would generally use OpenCL or other AMD-specific 
technologies like ROCm (Radeon Open Compute). CUDA-based applications 
are not compatible with AMD GPUs, and vice versa.
It's worth 
noting that some software applications and libraries support both CUDA 
and OpenCL, allowing users to choose between NVIDIA and AMD GPUs. 
However, the underlying GPU-specific code would need to be implemented 
separately for CUDA and OpenCL.
* in 2024 it is possible to run CUDA code in AMD GPUs
https://github.com/vosen/ZLUDA
* Is CUDA faster than MPI?
CUDA is fast, but only if your do a lot of parallel processing on matrices.
 CUDA can be very fast, but for some kind of applications. Data transfer
 in CUDA is often the bottleneck. MPI is suitable for cluster 
environment and large scale network of computers.
 
 
References
[1] NVidia Pascal 
https://wccftech.com/nvidia-pascal-gpu-gtc-2015/[2] https://community.amd.com/t5/ai-discussions/is-it-possible-to-use-cuda-technology-with-amd/td-p/643124