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Importance Of GPU In Data Science

By NIIT Editorial

Published on 29/11/2021

8 minutes

You need a system that can handle the computation required by machine learning. GPUs for machine learning purposes have become very common in recent years because GPU technology is advancing rapidly. Now the main question is; Are high-end graphics cards necessary for machine learning?

For machine learning, a good GPU is essential. Thanks to their thousands of cores, GPUs handle machine learning tasks better than CPUs. It takes a lot of computing power to train neural networks, so a decent graphics card is needed.

As you progress, you'll need a graphics card, but you can still learn everything about machine learning to use a low-end laptop. This article explains the need for a GPU, why the process is hardware-intensive, and why GPU is important for data science. The best online data science or data analyst courses could also assist in getting an in-depth understanding of this. 

GPU Stands For Graphics Processing Unit.

The GPU, or 'Graphics Processing Unit, is essentially a mini-computer dedicated solely to a single task. In contrast to a CPU, it can perform multiple tasks at the same time. GPUs feature their processor integrated onto their motherboard, along with RAM and a proper thermal design for cooling and ventilation. 

Is GPU Deep Learning Better?

An essential feature of a GPU is its ability to compute multiple processes simultaneously. Parallel processing is the ability of this product. CPUs are capable of completing their tasks sequentially with the help of their two cores. However, GPU offers a speedy process of delivering the tasks commanded, hence the efficiency increases compared to CPU. 

What Is The Impact Of GPUs On Data Science?

Getting our work done requires good processing power. It applies to laptops and desktops and the more extensive infrastructure we all depend on, such as servers, switches, and, of course, the network itself. You can learn about the same from the best data science courses online, or through the best certifications for data science.

CPU, or central processing unit, refers to the main processor within a computer, also known as the "brain" of the computer. Python libraries are becoming more efficient at utilizing a computer's available power in Data Science. However, CPUs will not suffice when running deep learning applications that require hundreds of millions or even billions of records.

GPUs are powerful parallel processing devices that enable organizations to run forecasting models across millions of product combinations for their retail stores to inform, plan, and optimize their warehouse operations. As GPUs are bringing more power to Data Science or for a data scientist, they create new opportunities for a data scientist, analytics departments, and companies.

GPUs process in parallel, whereas CPUs process sequentially. Even a large cluster of CPUs cannot train deep learning algorithms at the same speed as the exemplary architecture of GPUs.

Is A GPU Necessary?

  • It depends on the data science project. You need to consider the tradeoffs between speed, reliability, and cost.
  • You may get away without a GPU if your neural network is small in scale.
  • It might be worthwhile to consider investing in a GPU if the neural network of a data scientist includes tons of calculations involving hundreds of thousands of parameters.
  • Typically GPUs are a better bet for fast machine learning since, at its core, data science model training comprises simple matrix math calculations, the speed of which is enhanced when performed in parallel.

GPUs For Data Science: What To Look For?

When choosing a GPU for data science, it is necessary to consider several factors, tips, and tricks for the correct algorithm.

Cores Of Tensors

Using Tensor Cores reduces the time to calculate, multiply and add operations. They also reduce the need for repetitive shared memory accesses. They also reduce bottlenecks thanks to their high computational speeds.

Bandwidth Of Memory

The performance of a GPU can be determined by its memory bandwidth and algorithm. One of the most significant limiting factors in performance is memory transfers to the Tensor Cores. Sharing memory, a large L1 cache, and registers contribute to more efficient memory transfers to Tensor Cores.

Thermal Efficiency & Fan Design

Users of data science GPUs are familiar with thermal concerns since the number of computations they perform results in a great deal of heat generated by the graphics card. GPU manufacturers are working to improve their heat transfer efficiency to keep the GPU cool. For instance, the RTX 30 series includes a blower fan and a push/pull fan to keep the GPU cool. High-performance computing requires water cooling and the ability to add water cooling blocks.

Considerations Regarding Power

Power considerations, tips, and tricks are essential to keep in mind when choosing a GPU. For your machine to handle a single or multiple GPUs, it will need to manage its power. You might need to supply your computations with the power which you need by using PCIe extenders. These would be connected to your high-quality power supply. You can also reduce power consumption without sacrificing performance by properly cooling the GPUs.

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