I can help you with an article on Ethereum and its performance comparison between Ati Opencl and Nvidia Cuda Core. Here is a project:

Ethereum: AMD and NVIDIA – Comparison of activity

First of all, I would like to say that this thread does not concern discussions on the advantages of a video card manufacturer, because some people may want to claim their preferred choice according to various factors such as energy consumption, the costs and costs. Performance for a watts.

In this context, we are here to immerse you in the technical comparison of two popular calculation architects: AMD Opencl and Nvidia Cuda. The two technologies are used for parallel treatment in high performance calculation programs (HPC), but they have different differences in their design, efficiency and optimization.

Ati Opencl

AMD ATI OPENCL is an open source heterogeneous IT platform which allows developers to write a code that can run on various hardware platforms, including GPU and CPU. Architecture based on Cuda API, but with certain modifications based on specific AMD requirements.

One of the main advantages of ATR OpenCL is its ability to use multiple facets and other high -quality nuclei, in addition to the GPU. This makes it a popular choice for programs that require a combination of CPU and GPU processing power, such as scientific modeling and data analysis.

nvidia cuda

Nvidia Cuda is a patented parallel computer platform designed specifically for their GPU. This provides a large API and a set of tools for creators in order to write an efficient and high quality code that can perform NVIDIA equipment.

One of the main advantages of Cuda is its ability to optimize the model code for each GPU model, which ensures maximum performance and efficiency. In addition, the Cuda Cuda Runtime (NVCC) compiler creates an optimized machine code which can be directly executed by GPU, reducing general costs and improving overall performance.

Performance comparison

To give a more detailed comparison, consider a simple example: the image processing task 1024×1024 pixel with the target resolution 5120×3200. We will use OpenCl to write the Cuda nucleus, which performs the same operation in ATI and NVIDIA equipment.

Here are some examples of code in both languages:

C

// Opencl

#include

void my_opencl_kernel (float * data, int width, int height) {

#Pragma nuclei

Brandel_func (data);

}

int hand () {

// Name memory

Float Host_Data = New Float [1024 1024];

for (int i = 0; i <5120 * 3200; i ++) {

host_data [i] = 1.0f;

}

// Execute the nucleus

Cl :: Commandqueue tail (cl :: Deviceamd :: cl_device_id);

Cl :: the nucleus nucleus (line, my_opcl_kernel, my_opcll_kernel);

Brandel.setar (0, data);

// Get the GPU results

Float GPU_Data = New Float [1024 1024];

Brandel.Getoutput (0, gpu_data);

for (int i = 0; i <5120 * 3200; i ++) {

gpu_data [i] = host_data [i]; // exit to CPU

}

Delete [] data;

Delete [] GPU_Data;

Back 0;

}

'

` CPP

// cuda

#include

__Global_ void my_cuda_kernel (data float *) {

#Pragma core

kernel (data);

}

}

int hand () {

// Name memory

Float Host_Data = New Float [1024 1024];

for (int i = 0; i <5120 * 3200; i ++) {

Host_data [i] = 1.

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