Are you planning on installing a new GPU on your pc and can’t understand the difference between CUDA and Tensor cores? Rather than staring at your GPU spec sheet all day, take a look below and learn all the essentials.
CUDA core performs its functions one clock cycle at a time and enumerates calculations by multiplying two matrices and then adding the answer to another. Whereas the tensor core is capable of multiplying two 4×4 matrices per clock cycle and can perform multiple operations simultaneously, So basically tensor is an accelerated version of a CUDA core
Now that you know the difference between CUDA and Tensor cores, we hope your dilemma is solved. Whether you are a beginner or a pro, there is so much more you need to learn about these Cores so keep reading this article and get an even better insight into GPUs, the detailed functions of each core, and more.
What else distinguishes CUDA and tensor core?
As a PC builder, you might already know that a company named Nvidia is behind the development of both of these products. CUDA and tensor both are modern and steady options but are you wondering what other factors distinguish them? If your curiosity is piqued, keep reading this article to get a deeper insight.
Another major difference between the two cores is that a CUDA core can be considered a single-stream processor whereas a tensor core is more of a stripped-down part of it. you can say that it was separated from the CUDA core to perform a specialized function i.e FP16 matrix multiplication and addition.
CUDA core performs other functions as well but Tensor is a processor dedicated to this task alone and surprisingly, these fast and efficient tensor cores can not work on their own and they depend on CUDA cores to give them instructions.
Fact: A clock cycle is a period between two pulses during which circuits are synchronized.
What are GPU cores and why are they important?
Graphic processing units (GPUs) are a type of circuit computing technology whose main purpose is to do calculations and render graphics on your computer screen. They are used for both personal and business computations. GPUs are originally designed for parallel processing, 3D graphic creation, and realistic animations.
Even though they are mainly known for providing better lighting, 2D and 3D effects, shading, and a better gaming experience to their users, these GPUs are now starting to expand in artificial intelligence and creative visualization as well. Programmers and graphic designers use it widely because of its ability to accelerate video rendering and editing. Not only that but they are used in high-performance computing (HPC) and deep learning as well.
It was about 20 years ago that GPUs were only used for better gaming experience but as time has passed, programmers have realized its untapped potential, and now this graphic technology is used in a number of other programs e.g:
- Embedded systems
- Mobile phones
- Gaming consoles
- Workstations and personal computers
What is a CUDA core?
Computer unified device architecture (CUDA) cores are general-purpose cores used for rapid matrix calculations and parallel computing in electronic devices. Each CUDA core has an integer unit and floating point unit It is a pipeline that can do 32-bit floating point addition in a single clock cycle.
The CUDA Streaming Multiprocessor executes threads in warps (32 threads). There are a maximum of 1024 threads per block and 1536 threads per multiprocessor. A default CPU comes with around 16 cores meanwhile, CUDA can contain up to hundreds.
Initially, released in 2007 by Nvidia, CUDA is basically their equivalent of CPU cores that can process several calculations in your PC. It is a graphic card designed to work with programming languages such as C, C++, and Fortran.
Unlike previous developments, advanced graphic programming was forgone, making it more accessible for parallel programming specialists.
This is the same core used by Tesla, Volta, Kepler, pascal, maxwell, and fermi.
What does a CUDA core do?
Do you play graphically challenging games on your PC or are you a graphical designer always aiming for the best results? You’d be happy to learn that the CUDA core’s main function is to act as a CPU core in your PC and it focuses on providing a good gaming/ animating experience by performing graphics calculations with estimable accuracy. CUDA cores process all the data that enters and leaves the GPU, resolving them visually to the user.
They are also great at handling animations such as smoke, deres, fluids, fire, and much more like:
- Encryption, decryption, and compression
- Molecular dynamics
- Physical simulations and face recognition
- Medical scan stimulations such as MRI scans
- As an example, take a look at some of the small GPU cards and their cores below.
| GPU Card | CUDA cores | VRAM |
| GeForce GTX 1660 | 1408 | 6 GB |
| GeForce GTX 1060 3GB | 1280 | 3 GB |
| GeForce GTX 1650 Super | 1408 | 4 GB |
| Quadro P2000 | 1024 | 5 GB |
What is a Tensor core?
Tensor is also another one of Nvidia’s microarchitectures. It was released in 2017 and originally designed to work with volta. What distinguishes it is the fact that It is the first ever processor to have cores just for tensor calculations.
Having a specialized piece of technology in your PC that is solely responsible for graphic calculations will certainly speed up the process and assist you greatly. Its fabrication code is TSMC 12 nm (FinFET) and it not only speeds up the matrix multiplication but provides a better experience in gaming and content creation as well.
It is an optimized core specially used for AI-related functions that enable multi-precision computing, accelerated AI, data science, and graphic functions.
You can find a tensor core in Geforce RTX, Quadro RTX, and titan family.
As of now, the most advanced tensor core is the Nvidia V100 and you’d be surprised to hear that its advanced development offers the same effectiveness as 32 different CPUs.
We’d like to clear another common misconception by telling you that a tensor can’t be defined by physics. Why? because it is not an extension of vectors or scalar but rather the opposite. A tensor is a multidimensional set of numbers and this specific feature enables it to accelerate the entire process with incomparable speed.
The first generation tensor was introduced with volta architecture but with each new variation, the features and characteristics keep improving and the overall capabilities of the tensor core enhance.
What does a tensor core do?
A Tensor Core enables mixed-precision computing, which rapidly adapts calculations to improve throughput while maintaining accuracy. A tensor can also be considered a container for numerical information that you use within your PC.
Tensor cores enable two 4×4 FP16 multiplications to be added in FP16 or even FP32 matrices.
CUDA VS Tensor core, what are their properties?
Still, having trouble understanding? Look no further, we have comprehensively sorted out the advantages and drawbacks of each. By taking a look at them, you can value your options and see what each core brings to the table
| Advantages | |
| CUDA cores | Tensor cores |
| Ensure a more realistic gaming experience with 3D graphics | 4x-8x speed guaranteed, is capable of calculating 4×4 matrices at a time. |
| Each parallel processor in CUDA can run multiple concurrent thread blocks | faster than a CUDA core by 64 times in multiplication and 48 times in addition. |
| Can be used for deep learning because of its efficient parallel matrix multiplication | Speeds up neural network learning and high-performance computing (HPC) |
| Unified virtual memory | Convolution and multiplications are its specialties |
| Drawbacks | |
| CUDA cores | Tensor cores |
| Unit block is limited to 32 threads | The fast speed of tensors compromises the precision |
| CUDA is a proprietary tool of Nvidia and is only available from them. | You can never update the contents of a tensor core because they are immutable |
| Older CUDA versions used C syntax rules instead of C++ Even after updating the source code, your experience may be hindered because of ineffectiveness | Tensor cores can often harm the potency of the model and affect the ultimate result. |
| Costly with a lower computation speed |
Final thoughts
Understanding the difference between CUDA vs tensor cores and their potential benefits is extremely significant. It will enable you to comprehend the extent of their capabilities. In case we missed anything, some of your frequently asked questions are answered below.
Frequently asked questions
Q- how many CUDA cores do you need for deep learning?
A- Typically speaking, deep learning will certainly require high-level parallel processing meaning that you’ll need 4 cores and 8 threads at the very least.
Q- why is the tensor core faster?
A- what makes the tensor core’s speed incomparable is its ability to compute two 4×4 matrices i.e 64 operations at a single time.
Q- Can you use tensor cores for AI?
A- as Nvidia itself claims, the most recent tensor cores generation is likely to be the fastest on border array AI and high-performance computing (HPC)
Q- Do more CUDA cores ensure a better experience?
A- to put it simply, NO. The CUDA core will definitely make your game look realistic with 3D dimensions and impeccable detailing but the performance of a graphic card is most certainly not determined by the number of cores it contains.
We hope this article helped you understand the difference between CUDA and tensor core. Stay tuned for more PC-related content.
