Checking the newer Nvidia GeForce graphics cards, you’ll find the name CUDA core mentioned more than once. So, what is CUDA core?
Well, CUDA stands for Compute Unified Device Architecture and the core stands for GPU cores designed by Nvidia. In short, the CUDA core is a technology designed to take on multiple processing at the same time.
CUDA core is said to enhance game performance by a lot. Continue reading below to find if that’s really the case and how it works.
Table of Contents
What Is CUDA Core?
CUDA stands for ‘’Compute Unified Device Architecture’’. The CUDA core is a software layer that uses the parallel computing process.
NVIDIA GPU is the hardware that enables parallel computations while CUDA is the software layer that provides an API(Application Programming Interface) for developers.
Once you have an NVIDIA GPU, CUDA can be downloaded and installed from NVIDIA’s website for free. Developers use CUDA by downloading the CUDA toolkit, in with the CUDA toolkit come specialized libraries like cuDNN.
How Does CUDA core Work?
To know CUDA, we have to know the working procedure of Graphics Processing Units(GPUs) because GPU consists of hundreds or thousands of CUDA cores. So a GPU is a processor that is a combination of cores, is good at handling specialized computations, known as parallel computing.
CUDA core is a program based platform that does some specific work through programming. When work comes to be executed, then firstly that work is divided into some individual parts. After that, according to programming each smaller part of work is executed by each CUDA core that embedded on the GPU.
As all the CUDA cores are busy in doing individual tasks, no time is being wasted. CUDA core at last compels GPU to process all the individual tasks and get the desired output.
CUDA core is busy only on completing graphics work. It makes the graphics look more realistic as it provides high-resolution graphics. So where is graphics related works, there stands CUDA core with its parallel computation.
CUDA core VS CPU core
We know the CUDA core is embedded in GPUs. So it can be called GPUs core. There is a great deal of difference between CUDA core and CPU core. CUDA core works for GPUs, but CPU core works for CPUs.
GPUs work in parallel processing, so CUDA cores also execute in the parallel pipeline. Each CUDA core works for the same code that other does at the same time in parallel, so no need to wait for one core to accomplish its job. Rather all the CUDA cores can do their job together but independently. No extra time is needed and wasted in the CUDA core.
But in CPU cores, they prefer series computation. So all the cores need to wait for the completion of the job of the previous core. Each CPU core contains registers, cache memory, so each of them does their work in series one after another. That’s why sometimes the CPU core needs more time.
As a metaphor, if CPU can be considered as a hypercar, then CUDA core can be called a huge dump truck. If a simple operation is needed to execute than it is easy to complete. Then, the CPU core needs less time. But CUDA core works with mainly complex operations like graphics problems. So it needs more time.
As CPU core is now generalized core, so it normally works faster than CUDA core. Because the CUDA core has to maintain many operations at a time, but the CPU core doesn’t need it.
ecifically, CPU cores are general purpose unit for your PC or laptop, but CUDA core is a specific purpose, like graphics design, AI, unit.
Frequently Asked Questions and Answers
Is CUDA core same as CPU core?
There are thousands of CUDA cores in a GPU but that’s not the case for CPU cores. While the CPU can execute anything from the RAM memory, the CUDA cores are only responsible for performing the floating-point math.
Does more CUDA cores mean better?
Sadly, no. More CUDA cores don’t necessarily mean more performance. CUDA cores ensure better optimization, but the actual performance depends on the chipset model.
Can CUDA damage GPU?
No, CUDA cannot and will not damage your GPU. The only way to damage your GPU is to put it through graphics-intensive tasks without a good cooling system.
CUDA vs Tensor, what is better?
Operations involving large matrix perform better on Tensor. So, for AI workload, tensor is better. However, for general purpose computing tasks, CUDA is the way to go.
Although CUDA is not proven to increase anything dramatically, while playing those graphics intensive games, every frame counts. So, the optimizations offered by CUDA cores are something that will always come in handy, especially for gamers. Now that you know what is CUDA and what you can expect from it, you can make better decisions when purchasing a new GPU. Good luck.