Do CUDA Cores Help In Machine Learning?

Since the last decade, the GPU has become more prevalent in industries like HPC (High-Performance Computing) and the most well-known industry, gaming. The capabilities of GPUs have increased year after year, and they can now perform some amazing tasks. So, the question arises Do CUDA Cores Help In Machine Learning?

However, in recent years, deep learning has brought GPUs even more attention. It takes a long time to master deep learning models. Thus even powerful CPUs couldn’t handle as many computations at once. Because of this, GPUs just beat CPUs in this area due to their parallelism. However, let’s first learn a few facts:

Do CUDA Cores Help In Machine Learning?

Yes, Deep learning is boosted by the specialized cores known as CUDA cores that are built into Nvidia GPUs.

CUDA Cores Help In Machine Learning

What Is CUDA?

The term “Compute Unified Device Architecture,” or CUDA, was introduced in 2007. It is a method for achieving parallel computing and making the most of your GPU capacity in an optimal way, which leads to much-improved speed when carrying out activities.

The CUDA toolkit is a comprehensive set with a development environment for creating GPU-powered applications. The major components of this toolkit are a c/c++ debugger, compiler, and libraries.

It also has its own drivers for the CUDA runtime to interface with the GPU. Another programming language designed exclusively for giving instructions to the GPU is called CUDA. Another name for it is GPU programming.

Are CUDA Cores Equivalent To Processor Cores?

No, not at all. A CUDA core performs roughly two instructions per clock cycle (IPC), compared to between 35 and 40 (3700X) for an X86 CPU core. Also, consider that a GPU operates at 2GHz, whereas a CPU operates at 4.5GHz. Consequently, a CPU can perform the same tasks as about forty GPU cores.

A six-core CPU should accomplish the equivalent of 240 CUDA cores. Which GPU, then, has 240 CUDA cores? The GT 730, however, has 384 CUDA cores operating at 900MHz. In contrast, a GTX 1660 Super has 1408 CUDA cores that operate at 2000MHz. A mid-range GPU has roughly 10 times the raw computing capability of a CPU.

The cores must be somewhat compact to fit 1400 cores onto a single piece of silicon. While a CPU core has more than 500 million transistors and can perform hundreds of distinct specialized tasks, a CUDA core has “only” roughly three million transistors per core. A CUDA core is just a more powerful floating-point unit.

Is Higher CUDA Cores Better?

Modern GPUs have unified shader designs that are modular. In other words, they add additional modules to boost performance. SM is the moniker given to modules by Nvidia. Each module aids the rendering pipeline of a game by having both CUDA cores and certain fixed function units. Therefore, rendering performance generally increases as CUDA cores increase.

Only within the same set of cards is this comparison valid. Doing it between series is unfair. For instance, whereas GTX 1060 has 1280 cores, GTX Titan has 2688 cores. However, Titan is only quicker on a few CUDA computational tasks, whereas GTX 1060 is faster in games.

Even though it only has half as many cores as Titan, the GTX 1060 is still a stronger card overall. Perhaps the difference in GPU frequency is the main explanation, but there are additional architectural benefits.

The balance between CUDA core, ROP, TMU, and memory bandwidth is crucial for particular games (or algorithms). Increasing the number of cores won’t assist if the memory bandwidth substantially stifles the algorithm.

Why Do People Prefer GPU Over CPU For Deep Learning Other Than The Reason Being It’s Faster?

You have two tasks to complete: On a piece of paper, holes must be punched. Make ten thousand of these holes. Additionally, you must drill a hole of the same size into a sheet of metal 1/4 inch thick.

The CPU is comparable to a heavy-duty hole puncher that can drill through metal slabs. To drill a hole into something that is not thicker than an inch, let’s say the whole punch machine needs two seconds.

The GPU can be compared to a device built specifically to punch 10,000 holes into a piece of paper simultaneously. And let’s say it takes this device 100 seconds to punch a set of 10,000 holes.

The industrial hole punch machine can quickly create holes on a piece of paper because it can readily punch holes even into metal blocks. The only drawback is that drilling all the holes you need will take 2 X 10,000 seconds, or 20,000 seconds, at a hole-per-second rate.

The same task could be completed in 100 seconds using our specialized hole punching machine (GPU). However, it cannot cut holes into metal sheets; it can only do it on paper. The two machines are, therefore, each quick in and of themselves.

Depending on the task, one may be faster than the other. On a CPU, an addition operation might be finished significantly faster than on a GPU. However, GPUs are designed for higher throughput rather than faster operation. Instead of performing a single complex operation, they can simultaneously perform basic operations on numerous parts.

Using the CPU, you would need to add 5 to the first element, then to the second element, and so forth to add the number 5 to a matrix of size 100 x 20. The identical addition may be made to all 2000 elements of the matrix at once using the GPU.

And at its core, neural networks are represented as matrices. Training is essentially arithmetic operations performed on matrices. For machine learning tasks, GPUs are preferred over CPUs for this reason.

GPUs are utilized for gaming for the same reason. Processing each pixel individually on a CPU won’t work when you need to render an image with 1920 x 1080 pixels. You require sophisticated machinery that can process lots of pixels. Here, a GPU is just what we require.


As a result of everything, we have learned about Do CUDA Cores Help In Machine Learning? It is obvious that Nvidia currently dominates the GPU market. However, I hope that AMD eventually catches up, or at the very least makes some notable improvements in their upcoming lineup of GPUs, as they are already doing an excellent job with their CPUs, the Ryzen series.

As we make discoveries and advancements in deep learning, machine learning, and HPC, the potential for GPUs will grow significantly in the next years. Since their prices are also decreasing, GPU acceleration will always be useful for many developers and students who want to enter this industry.

Frequently Asked Questions

Do machine learning applications use CUDA?

Various machine learning and analytics software libraries are available from NVIDIA to speed up complete data science pipelines solely on GPUs. The more than 15 years of CUDA development have made this work possible.

What are the benefits of CUDA cores?

Similar to how your CPU could have two or four cores, CUDA Cores are parallel processors, and Nvidia GPUs can have hundreds or thousands of them. The cores are in charge of processing all the data going into and coming out of the GPU and working on the game graphics calculations that the user sees.

Is machine learning better with more cores?

GPUs cannot be used to train machine learning models. Hence CPUs are more useful in this situation. Whenever you have a lot of data in the dataset for training the machine learning model, you can employ these CPU cores for faster machine learning training in any machine learning project.

Can data science be done with 8 cores?

A minimum of four cores and eight threads is suggested. Go for 6 cores, 8 cores, or greater if money is not an issue.

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