Choosing the Right GPU for Data Science
There are many different fields of research and just as many different graphics cards that can work better than others depending on what you are trying to do. So, what's the best GPU for data science? Let's first take a look at what data science is.
What is Data Science?
Data science is the field of study using scientific methods and processes to combine domain, programming, and mathematics expertise in order to pull insights from raw data.
These data science insights can be used by machine learning algorithms in order to produce artificial intelligence (AI) systems. These AI systems then perform tasks that would otherwise require a human’s intelligence. In turn, these AI systems generate usable data for analysts, scientists, and business users alike to convert into tangible value – like products, services, entertainment, etc.
Data science is a means of collecting, organizing, and valuing data through scientific methods to analyze and make sense of various phenomena in the world. This could look like anything, including raw data collected from human-caused vehicle collisions. The data can then be processed by self-driving cars to avoid accidents. Or another application could be in mapping out genetic mutations and adaptations of household cats in the United States in order to create higher quality cat food for cat owners.
These are just a few of the many applications of data science, machine learning, and AI. Any area where data is collected and analyzed can be optimized using data science. And as you might imagine, all of this data processing requires the right graphics cards to make it all happen!
Let’s unpack some of the best ways to choose the right graphics card for data science.
How Graphics Cards Are Used In Data Science
NVIDIA 900-2G500-0040-000 Tesla V100S - 32GB PCIe Passive Graphics Card
Data science requires intense computational requirements, and having the right GPU for data science computations will make everything much easier. But what features are important for considering what GPU to buy for data science? The GPU RAM? The number of cores? And what about the cost of bringing all this computation to life? Let’s unpack some of this information on how GPUs are used in data science.
GPU Hardware Considerations
Typically, the order one should take into consideration for data science goes like this:
- Random Access Memory (RAM)
- Swap capabilities
- Read and write speeds (in some cases)
- Graphics cards
You'll notice that your graphics card, or GPU, is at the bottom of the list when thinking about hardware requirements for data science. GPUs are able to process information in a number of unique ways when compared to the CPU.
CPUs are latency optimized while GPUs are bandwidth optimized meaning the CPU can fetch some memory (packages) in your RAM quickly while the GPU is slower in doing that (much higher latency). However, the CPU needs to go back and forth many times to do its job while the GPU can fetch much more memory at once.
This is significant because it means the CPU is good at fetching small amounts of memory quickly while the GPU is good at fetching large amounts of memory.
In terms of memory bandwidth, CPUs have about 50GB/s while the best GPUs have 750GB/s – meaning the GPU is better to use than the CPU for data science.
What makes GPUs well suited for data science and deep learning is:
- High bandwidth main memory
- Hiding memory access latency under thread parallelism
- Large and fast register and L1 memory which is easily programmable
Essentially, a data science graphics card will help accelerate your applications and research!
And who doesn't want to speed up and simplify their work?
What to Look for in a GPU for Data Science
There’s a number of important things to consider when deciding on which GPU to use for your data science needs.
Tensor Cores reduce cycles needed for calculating multiply and addition operations. They also reduce reliance on repetitive shared memory access. And they reduce bottlenecks because of their fast computational speeds.
One of the best indicators for a GPU’s performance is memory bandwidth. We know this because memory transfers to the Tensor Cores are one of the greatest limiting factors in performance. Shared memory, L1 Cache, and amount of registers all help to enable faster memory transfer to Tensor Cores
Fan Design & Thermal Efficiency
As you might imagine, thermal issues are a regular concern of data science GPU users since the amount of computations results in a lot of heat being produced by the GPU. Many GPU manufacturers are working on improving their efficiency with heat transfer to keep the GPU cool. For example, the RTX 30 series features both a blower fan and a push/pull fan to help cool the GPU. Water cooling, and the ability to add in water cooling blocks is a must-have for high performance computing.
There are a number of power considerations to know about for your GPU.
Your machine will need to be able to properly handle the single or multiple GPUs you’ll be integrating. PCIe extenders connected to your high quality power supply may be able to help provide the power you need for your computations. And if the GPUs are properly cooled, you’ll help to reduce the power consumption as well without sacrificing performance.
So which graphics cards are recommended?
NVIDIA Offers Solutions for Data Science Graphics Cards
NVIDIA is definitely at the top of the industry for providing data science, deep learning, and machine learning graphics cards. The NIVIDIA A100 Tensor graphics cards for example, are some of the best in terms of performance and computational ability (and without a significant price tag) considering everything you’re getting. And they even provide CUDA – a parallel computing platform and programming model to speed up computing applications using their GPUs.
The Importance of CUDA for Data Science
CUDA Toolkit Documentation
For data science, CUDA is an absolute must-have. You could get away without using it, but you are going to be outpaced by competition and end up kicking yourself for not factoring CUDA into your hardware purchase decision. Having the ability to use CUDA is a major decision-making factor in choosing your GPU because CUDA is only available on NVIDIA GPUs.
So, when it comes to searching for a GPU for data science, we definitely recommend choosing an NVIDIA GPU.
The NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration at every scale for AI, data analytics, and high-performance computing (HPC)
When it comes to high-level performance, no one else really compares – especially with NVIDIA's Ampere series. The NVIDIA A100 Tensor Core GPU is great for high performance computing in AI, data analytics, and data science allowing production at scale with 20x higher performance over the prior generation of GPUs.
Using this particular GPU, large AI models like BERT can be trained in just 37 minutes on a cluster of 1,024 A100s!
However, the A100 is not the only NVIDIA GPU out there if you’re looking for something else to use for data science.
Your Guide to Purchasing a GPU
Here’s a list of the things you need to know or determine before purchasing an NVIDIA GPU.
NVIDIA GeForce RTX 30 Series GPUs
The Architecture of the GPU
When considering NVIDIA GPUs, it's important to recognize the different architectures each GPU uses. Since 2006, NVIDIA has released eight different GPU architectures, with the first four largely being phased out at this point.
This leaves us with Pascal (released in 2016), Volta (2017), Turing (2018), and Ampere (2020) architectures to sort through. This helps us narrow down exactly what we are looking for in an NVIDIA data science GPU.
To narrow our search down further, we come to the first thing you need to look for when choosing the right graphics card for data science: tensor cores.
A tensor core is a processing core that focuses on multiplying and adding matrices. Tensor cores were not added to NVIDIA GPUs until Volta (2017). So, we can now eliminate searching through the Pascal architectures and zero in on what we need for our data science GPU.
An important note: not all three remaining architectures will necessarily have tensor cores, so you will want to double-check your research as you choose a GPU geared toward data science.
As you look at NVIDIA GPUs options you will notice the graphics cards labeled RTX will most often have tensor cores while those labeled GTX will most often NOT have tensor cores. This should also help speed up the research and selection process for your GPU.
Now, another important factor to take into consideration while choosing the right graphics card for data science is, of course, the price.
NVIDIA has three major brands of graphics cards that serve data science well: Quadro, Titan, and GeForce. When thinking about price, you need to determine what budget you have because Quadro and Titan graphics cards, while being designed with data science in mind, are some of the most expensive GPUs offered.
Therefore, if you have a smaller budget to work with, then the GeForce brand of NVIDIA GPUs are the way to go. If you have the budget for either Quadro or Titan GPUs, though, they are well worth the price since they are tailored towards data science.
Another important factor for choosing the right graphics card for data science is the amount of memory bandwidth provided by your GPU.
Once you have selected a GPU with tensor cores you will want to look at the memory bandwidth for the card. Tensor cores drastically speed up the multiplication and addition of matrices, as we already mentioned.
However, tensor cores can only speed up the process of parallel computing due to their memory bandwidth. The more memory bandwidth, the faster they compute.
It can be a little tricky to determine the memory bandwidth of a GPU just by looking at a product description, so be careful to do some research on the memory bandwidth for the GPU you are looking at. Remember, as with tensor cores, more is better!
One last factor to keep in mind that can make a huge difference: power limitations.
Now, if you are working in a location where power limitations are not an issue and you can pull as much power as you need for your computations, then this may not be an issue. If you are at your home or even in certain business locations, then you may need to pay close attention to how much power you can draw for your computer.
For example, the NVIDIA GeForce RTX 3090 requires 350W of power but a recommended 750W of dedicated power just for the graphics card to ensure maximum efficiency.
To put this into perspective, if you are working from home your circuit breaker can typically deliver 1800W of power reliably. In our example here, the GPU alone would be draining ~42% of your available power on that entire circuit!
Depending on your setup, this may or may not be a deal-breaker for you. However, it is important to pay attention to how much power your computer system will be drawing so you do not risk tripping your circuit breaker or, worse, your computer system!
While power limitations are something to keep in mind, it is also worth mentioning that the more computation power your GPU has the more power it will pull to do so. Finding a balance between power limitations and raw computational power for your data science endeavors will be an important part of the process.
We hope this guide has been helpful in helping you choose the right graphics card for your data science projects and applications. If you’re looking for any video cards for your next project, we offer many GPUs in all sizes and for all budgets.
View Some Of Our Other Helpful ArticlesWe hope this quick guide to choosing the right graphics card for data science helps you make the best decision for all your deep learning and machine learning projects.
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