What is a GPU kernel?

As you look more in depth into the processing world of accelerators there are some terms to get your head around

Sebastian Kozlowski

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As you look into the workings of even the best graphics cards, there are some buzzwords that might be harder to understand. So we look at what is a GPU kernel.

A GPU kernel is a piece of code that is executed in parallel by multiple threads on an accelerator such as a GPU. These kernels are designed to handle highly parallelizable tasks that take advantage of the many cores present in modern GPUs.

Another way of looking at a GPU kernel is as a function that can be called from a host program to perform calculations or other operations on the GPU. These functions are optimized for execution efficiency on the GPU, usually by breaking large tasks down into smaller sub-tasks that can be executed in parallel by multiple threads.

GPU kernels are widely employed in graphics processing, scientific computing, machine learning, and other high-performance computing applications. By offloading computational tasks to the GPU, these programs can achieve significant performance gains compared to running identical code on a CPU.

They’re particularly beneficial when dealing with large data sets requiring lots of computation since they take advantage of the parallel processing power of GPUs to accelerate operations and reduce overall execution time.

Examples of GPU kernels

One example of it is Nvidia’s CUDA cores on its GPUs. Which are optimized to work together to work through the workload to create the graphics at the end.

The same is for OpenCL which is the Open Computing Language framework. Which is open to be used by a range of platforms and companies.

Overall, a GPU kernel’s primary benefit lies in its capacity for performing highly parallelizable tasks on an enormous scale, making it indispensable for many modern computing applications.

A GPU kernel is an application or function designed to run on a GPU that executes in parallel across multiple threads to accelerate computations. GPUs are specialized hardware devices optimized for parallel computing, and kernels form the cornerstone of GPU computing.

They significantly reduce overall execution time and accelerate complex calculations in fields such as image processing, data analytics, scientific simulations, and machine learning, just to name a few!