AMD Server Graphics Card - A Technical Guide

When it comes to server rendering or accelerating applications that require intensive graphical processing, AMD has been a reliable choice for many years. With the advent of AMD’s server graphics cards, more and more enterprise level businesses have been able to harness the power of the graphics processing units (GPU) that AMD is renowned for. In this technical guide, we will delve into the technology behind AMD server graphics cards and provide code examples to illustrate how to use them.

Architecture

AMD’s server graphics cards have been built on the Graphics Core Next (GCN) architecture which was originally launched in 2011. GCN is a highly parallel architecture that is built to handle compute workloads efficiently. It has been designed with high-performance computing in mind, allowing for large compute workloads to be executed in parallel.

GPU Boost Technology

One of the features that make the AMD server graphics cards stand out is the GPU Boost technology. GPU Boost is a feature that dynamically increases the GPU clock speed when there is power headroom, allowing for higher clock speeds and, hence, better performance. It works by monitoring the power usage and temperature of the GPU and adjusting the clock speed accordingly.

Using AMD Server Graphics Cards

Setting up an AMD server graphics card requires little more than plugging it in, connecting power and installing the appropriate drivers. The most commonly used drivers for server graphics cards are the proprietary drivers provided by AMD, which can be downloaded from their website. The drivers provide support for all the features of the graphics card, including GPU Boost and OpenCL.

OpenCL

OpenCL is an open standard parallel computing language that allows developers to write programs that run on multiple devices, including AMD server graphics cards. It can be used to accelerate many types of applications, such as image and video processing, scientific simulations and machine learning. OpenCL programs can be written in C, C++ and Fortran, making it accessible to a wide range of developers.

Sample Code

Here is a simple C++ program that demonstrates how to use OpenCL on an AMD server graphics card:

#include #include

int main() {

// Create OpenCL context std::vectorcl::Platform platforms; cl::Platform::get(&platforms); cl_context_properties cps[3] = {CL_CONTEXT_PLATFORM, (cl_context_properties)(platforms[0])(), 0}; cl::Context context(CL_DEVICE_TYPE_GPU, cps);

// Get devices std::vectorcl::Device devices = context.getInfo();

// Create OpenCL command queue cl::CommandQueue queue(context, devices[0]);

// Create OpenCL buffer float data[] = { 1.0, 2.0, 3.0, 4.0 }; cl::Buffer buffer(context, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR, sizeof(data), data);