When it comes to graphic processing units (GPUs), two terms that are commonly used are Tensor Cores and Cuda Cores. Although these two types of cores share some similarities, they have different functions that cater to specific needs. In this article, we will cover the basics of Tensor Cores and Cuda Cores, how they differ from each other, and which one is better for a specific scenario.
What are Tensor Cores?
Tensor Cores are a type of core that is present in the NVIDIA Volta and Turing architectures. They are designed to perform matrix computations that are essential in machine learning and artificial intelligence (AI). These cores execute matrix multiplication at a much faster rate than the traditional CUDA cores. Additionally, they perform mixed-precision calculations that allow the Tensor Cores to achieve peak performance while minimizing energy consumption.
Tensor Cores also use two data types, such as float16 and float32, to accelerate artificial intelligence workloads. These data types provide high-precision calculations and efficient processing for AI models. In addition, Tensor Cores can perform operations on large tensors, allowing them to speed up deep learning training and inferencing by a significant margin.
What are CUDA Cores?
CUDA cores are standard processing units that are present in NVIDIA GPUs since the introduction of the CUDA platform in 2007. They perform mathematical operations efficiently and are generally used for graphics rendering and high-performance computing (HPC). These cores can handle single-precision and double-precision calculations and are designed to work on a wide range of applications like gaming, video rendering, protein folding, and scientific simulations.
CUDA cores are versatile and can be used for both general-purpose computing and specialized computation. They can be programmed to execute a wide range of tasks, including audio processing, video processing, and simulations. Additionally, CUDA cores can handle tasks that require high levels of parallel processing, making them ideal for tasks that require faster processing times.
Tensor Cores vs. CUDA Cores
While Tensor Cores and CUDA cores are similar in some ways, they differ in their functionality and applications. When it comes to performing complex matrix computations required by AI and deep learning models, Tensor Cores outperform CUDA cores. They provide higher accuracy and faster performance while consuming less power, which makes them ideal for AI training and inferencing.
On the other hand, CUDA cores are better suited for general purpose computing tasks like gaming, 3D modeling, and video processing. They can handle a wider range of tasks, and with the right programming, they can outperform the Tensor Cores when it comes to specialized tasks.
Another significant difference between Tensor Cores and CUDA Cores is their precision. Tensor Cores mostly work with float16 and float32 data types which are perfect for AI computations that require precision, but they are not ideal for tasks that require high precision, like scientific simulations. On the other hand, CUDA cores can perform single-precision and double-precision calculations, making them ideal for scientific simulations.
Which one should you choose?
The choice between Tensor Cores and CUDA Cores depends on the application you want to run. If you need to run AI or deep learning models, Tensor Cores are the way to go. They can accelerate matrix multiplication, provide higher accuracy, and consume less energy. If you are looking for versatility and want to run multiple applications, CUDA cores are more suitable. They offer a wider range of performance and can handle many general-purpose computing tasks.
It’s worth noting that having both Tensor Cores and CUDA cores in a GPU is even better. It allows you to execute general-purpose computing tasks and AI computations on the same device without sacrificing performance.
Tensor Cores and CUDA cores are both essential for different applications. Tensor Cores provide faster performance and better energy efficiency for AI and deep learning applications, while CUDA cores are versatile and can handle a wide range of general-purpose computing tasks. The choice between them depends solely on the application you want to run. Choosing the correct type of core ensures that you get the best performance for your application, saving time and resources in the process.