How Important Are CUDA Cores For Graphic Designing?

How Important Are CUDA Cores For Graphic Designing?

Nvidia’s parallel processing platform is known as CUDA. CUDA Cores, like AMD’s Stream Processors, are the processing units within a GPU.

CUDA stands for Compute Unified Device Architecture. It is the term given to the parallel processing platform and API that is used to directly access the Nvidia GPU instruction set.

Unlike DirectX and OpenGL, CUDA does not need developers to learn complex graphics programming languages; instead, it works with common programming languages like as C, C++, and proprietary Nvidia technologies.

Learn graphic design online through Blue Sky Graphics online graphic design course.

CUDA Cores

If you have ever glanced at the specifications sheet for your Nvidia graphics card, you have almost certainly come across this phrase.

Consider the following example to better understand how CUDA cores operate. Consider the processor to be a water tank. If you wish to empty the tank, you will need to utilise pipes.

If you connect additional pipes, you will naturally be able to empty the tank quicker. CUDA cores serve as these pipelines to the CPU. The greater the number of CUDA cores, the quicker the processing may be done.

How Important Are CUDA Cores For Graphic Designing
How Important Are CUDA Cores For Graphic Designing

Modern Nvidia GPUs are equipped with three kinds of processor cores:

  • CUDA cores
  • Tensor cores
  • Ray-Tracing cores

Each core is intended to serve a particular function. Ray-Tracing cores are unique to Nvidia RTX graphics cards, while AMD does not have any GPUs with Ray Tracing Cores. AMD’s forthcoming RDNA 2-based GPUs, on the other hand, will enable Hardware Accelerated Ray Tracing.

CUDA cores were included in graphics cards beginning with the Tesla architecture. CUDA cores are present in all Nvidia GPUs, including Tesla, Fermi, Kepler, Maxwell, Pascal, Volta, Turing, and Ampere. However, the same cannot be true for Tensor or Ray-Tracing cores.

The earliest Fermi GPUs had up to 512 CUDA cores, which were arranged as 16 Streaming Multiprocessors with 32 cores apiece. The GPUs could handle up to 6GB of GDDR5 memory.

The number of CUDA cores was quadrupled with the Kepler design. The Kepler architecture could accommodate up to 1536 CUDA cores. It was built using 28nm fabrication technique.

Nvidia continued to add CUDA cores with each generation after that. The block diagram of the Nvidia Quadro GP100 looks like this. It was included in Nvidia’s Pascal architecture, which was launched in 2016.

Each stream multiprocessor in the Maxwell and Pascal architectures has 128 CUDA cores (SM). In Maxwell architecture, the integer unit was reduced, and the specialised multiplication unit was removed.
Nvidia’s Turing architecture included many improvements to GPUs. The block diagram of the TU102 GPU looked like this.

The number of CUDA cores per SM was decreased from 64 to 64. (from 128). Tensor and Ray Tracing cores have been added. TSMC’s 12 nm manufacturing technique was utilised. Starting with the Turing architecture, the integer and floating-point units were split.

The most current Ampere design included Ray Tracing Cores of the second generation. The GPU of the GA100 has 128 SM. The Ampere GA102 is equipped with 10,752 CUDA cores. Each core now consists of two FP32 processor units (Units which carry out 32-bit floating-point operations).

The fascinating thing about these CUDA cores is that they can handle both integer and floating point operations. This means that each CUDA core in the Ampere architecture can perform two FP32 operations or one FP32 and one INT operation per clock cycle.

The following is a block diagram of the GA102 GPU, which is based on Nvidia’s newest Ampere architecture.

The next generation of Nvidia GPUs will almost certainly be built using the 5 nm manufacturing technology. This will further reduce die size, lowering power needs and increasing clock rates to exceed 2 GHz.

As developers get a greater knowledge of the newer architectures, they will be able to better optimise their games and applications to increase performance even more.


What effect do CUDA cores have on performance?

Nvidia GPUs have hundreds or thousands of CUDA cores. When it comes to processing power, there are many factors to consider when evaluating a GPU’s performance. GPU Clock rates, GPU Architecture, Memory Bandwidth, Memory Speed, TMUs, VRAM, and ROPs are just a few of the factors that influence GPU performance.

VRAM stores assets, textures, shadow maps, and any other data that is being processed by the GPU. Graphic cards store this data in VRAM since it is considerably quicker to access it from VRAM than from DRAM, SSD, or HDD.

Many variables influence the quantity of VRAM required by your system (like resolution). Most current graphics cards have VRAM capacities ranging from 2GB to even 24GB (see at you, RTX 3090).

When it comes to clock speeds, there are two to consider: the core clock and the memory clock. The GPU’s core clock is the pace at which it works. The memory clock, on the other hand, is the pace at which the GPU’s VRAM operates. The core clock is comparable to the clock speed of the CPU, while the memory clock is comparable to the speed of the RAM.

The majority of CPUs in the mainstream market have two to sixteen cores. This allows them to carry out activities in simultaneously. There are many things that must be computed in simultaneously when it comes to graphical calculations. When it comes to GPUs, what you term a core is really simply a Floating Point Unit in comparison to CPUs.

A GPU core cannot fetch or decode instructions; it can only do computations. The number of CUDA cores in a contemporary GPU is often in the thousands.
When comparing graphic cards from various generations and architectures, things become a bit more complicated. The Nvidia GTX 1070, for example, has almost the same amount of CUDA cores as the GTX 780, but the RTX 2060 has less CUDA cores than the GTX 780. This is not to say that the GTX 780 is superior than the GTX 1070 or RTX 2060 in any way.

This performance disparity is caused by differences in architecture, transistor size, and manufacturing method across GPU generations. The CUDA core’s speed is heavily influenced by the size of the fabrication and the GPU architecture. As a result, a single CUDA core of the current generation is much more powerful than its predecessor.