CUDA Cores: What, Comparison & Importance

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Ali Rashid Khan

At HI Digital I test and write about different hardware configurations to show other PC users what they can expect from their build. My end goal is to educate the masses to make the right decision when they are looking for new PC parts to buy

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GPU is a very crucial component of your PC build that contributes greatly to the overall performance bar. Many important parameters run a graphics card efficiently. Such an important factor is CUDA cores. Modern GPUs contribute to the processing along with the rendering through these CUDA cores. Therefore, you need a good understanding of these parallel processors to make an educated decision while buying a new GPU.

• Cuda cores are parallel processing units found specifically in NVIDIA GPUs.
• They have a significant impact on the performance of a GPU.
• The role of Cuda cores in modern Nvidia GPUs is vast. They ace in executing parallel computing along with facilitating various other tasks.
  

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What Are CUDA Cores?

CUDA cores are parallel processing units found in NVIDIA GPUs. These cores enable GPUs to contribute to processing along with rendering applications by performing calculations and executing tasks in parallel with the CPU. Moreover, CUDA cores are specifically designed for CUDA programming, which allows developers to harness the power of GPUs for general-purpose computing tasks other than just rendering^[1].

Furthermore, CUDA cores work together to process data simultaneously, which significantly speeds up computations compared to traditional CPU processing. Each CUDA core is capable of executing multiple instructions simultaneously which makes GPUs highly efficient for parallel workloads^[2].

Tensor Cores Vs CUDA Cores

Tensor Cores and CUDA Cores are two different types of cores found in NVIDIA GPUs. Both of these cores serve distinct purposes in the field of parallel computing. Below is a table that compares these two:

What Do CUDA Cores Do?

The role of CUDA cores in modern NVIDIA GPUs is vast. They ace in executing parallel computing along with facilitating various other tasks. Some of the important roles of these cores are as follows:

• Parallel Processing: CUDA Cores are designed to handle parallel processing tasks efficiently. They offload the CPU workload and enable the GPU to perform multiple calculations simultaneously. Moreover, they make it possible to execute complex calculations in a fraction of the time that it would have taken by the CPU alone^[3].
• Graphics Rendering: CUDA Cores were also initially developed for graphics processing. Other than parallel computing, they serve as the backbone of GPU-based rendering. They handle various graphics-related tasks, such as vertex processing, pixel shading, geometry processing, and texture mapping. Furthermore, CUDA Cores excel at rendering realistic 3D graphics, enabling smooth and immersive visual experiences in gaming, virtual reality, and computer-aided design (CAD)^[4].
• General-Purpose GPU Computing (GPGPU): CUDA Cores can be utilized to accelerate a wide range of computational tasks beyond graphics which includes scientific simulations, machine learning, data analytics, and much more. By leveraging the parallel processing power of CUDA Cores, complex computations can be divided into smaller, parallel tasks that are executed simultaneously, resulting in marvellous performance gains^[5].
• CUDA Toolkit and Programming: Additionally, if you are a developer then you can use the CUDA Toolkit which contains programming models and libraries for GPU programming. With the help of this toolkit, you can write code that exclusively runs on CUDA-enabled GPUs. In this way, you can harness the power of CUDA Cores and unlock the parallel processing capabilities of GPUs^[6].

Stream Processors Vs CUDA Cores

Stream Processors and CUDA cores are almost the same things named differently by two leading GPU-producing giants i.e., AMD and NVIDIA, respectively. Let’s take a look at these two:

RTX CUDA Cores Comparision

The below table enlists the number of CUDA cores contained in different RTX GPUs to provide you with a comparison of different latest RTX graphics cards based on CUDA cores:

Do CUDA Cores Matter?

CUDA cores are parallel computing workhorses of modern NVIDIA GPUs. So, they have a significant impact on the performance of a GPU. These cores enable the GPU to offload computation-intensive tasks from the CPU and process them on its CUDA cores^[7]. Therefore, in this way, highly intensive computation tasks can be completed within a fraction of the time that would have been taken by the CPU if CUDA cores were not there.

Additionally, CUDA cores also contribute to general-purpose GPU computing (GPGPU) and graphics rendering^[8]. Therefore, CUDA cores are very important performance parameters that greatly influence the performance of a graphics card.

How To Enable CUDA Cores?

CUDA cores are intrinsic and built-in in the NVIDIA GPUs. Every time you run a CUDA-enabled application, your CUDA cores automatically come into action. However, you can ensure that CUDA cores are running properly on your NVIDIA GPU by checking for and installing the latest GPU drivers and CUDA toolkit.

Related Helpful Resources By Tech4Gamers:

• VRAM
• GDDR6
• Tensor Cores
• Ray Tracing
• Bottleneck

References:

1. Jacob Knoles (California State University Chico). NVIDIA CUDA Parallel Programming and Computing Platform. Retrieved from: https://physics.csuchico.edu/~eayars/code/CUDA_presentation.pdf
2. Kayvon Fatahalian. (CMU School of Computer Science). HOW A GPU WORKS? Retrieved from: https://web.engr.oregonstate.edu/~mjb/cs575/Handouts/gpu101.2pp.pdf
3. Princeton Research Computing. GPU COMPUTING. Retrieved from: https://researchcomputing.princeton.edu/support/knowledge-base/gpu-computing#mps
4. Shaohao Chen (Boston University). Introduction to High-Performance Computing. Retrieved from: https://www.bu.edu/tech/files/2017/09/Intro_to_HPC.pdf
5. North Dakota State University. Introduction to Neural Networks. Retrieved from: https://kb.ndsu.edu/page.php?id=133762
6. Justin Hensley (MIT). Hardware and Compute Abstraction Layers For Accelerated Computing
   Using Graphics Hardware and Conventional CPUs. Retrieved from: https://archive.ll.mit.edu/HPEC/agendas/proc07/Day3/10_Hensley_Abstract.pdf
7. Oregon State University. GPU 101. Retrieved from: https://web.engr.oregonstate.edu/~mjb/cs575/Handouts/gpu101.2pp.pdf
8. Mike Houston (Stanford University). A CLOSER LOOK AT GPUS. Retrieved from: https://graphics.stanford.edu/~kayvonf/papers/fatahalianCACM.pdf

Ali Rashid Khan

[How-To Guides Expert]

Laiba, our tech guru at HI Digital, simplifies and crafts How-To Guides with a Google IT Support Certificate. Beyond tech, she captures the city’s aesthetics through her lens. Join her journey where tech meets creativity!

Get In Touch: Laiba@hidgt.com