Nvidia Metrics and more

 

[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