Last updated on September 14th, 2022

In deep learning, GPUs have been gaining massive popularity, and for data scientists struggling to carry out HPC codes, this processor is a known term. Going back to the history of GPU development, the combination of CPU and GPU led to the formation of GPGPU, which is the General-Purpose Graphics Processing Unit.

GPGPU was initially developed to extract a feature that could better process the image graphics or data. Still, later it was found to be better for scientific computing data as the processing of images needed matrices.

Further, when the first algorithm of GPU was implemented, the researchers found GPU to be the faster processor. And after that, NVIDIA came into play with CUDA as its high-level language, which was used for writing programs for processing image graphics or data.

In the present times, GPUs potential is highly recognized in the field of deep learning, and here’s why this is happening.

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Importance of GPU for Deep Learning

In deep learning implementation, the training phase is the most extensive and resource-heavy phase. Though this phase can quickly be completed in less time if the models have fewer parameters, when this number increases, the time involved in training gradually increases. Thus, resulting in double expenditure.

With GPUs, you can successfully reduce these expenditures, allowing you to work with models with multiple parameters quickly and efficiently.

GPUs can deliver such performance because it gives the liberty of conducting the training tasks parallelly and distributing tasks over groups of processors while performing computational operations.

In addition, GPUs are also used for performing computational and target tasks, which is a tough nut to crack for non-specific hardware. Thus, the bottlenecks occurring because of computation limitations no longer exist.


Also Read: Cloud GPUs: The Cornerstone of Modern AI

Things to Remember While Choosing GPU for Deep Learning


Licensing requirements for different GPUs are also different. For instance, as per the NVIDIA guidelines, some chips are prohibited for use in the data centers.

As per the updates of licensing, for the use of consumers, CUDA software has certain limitations. Also, these licensing requirements require transition to the GPUs supported by production.

Interconnection of GPUs

The scalability of any project is highly dependent on the interconnecting GPUs. In addition, the interconnected GPUs decide whether or not more than one GPU and distribution strategies can be utilized.

Interconnecting GPUs do not support consumer GPUs. For example, Infiniband connects various GPUs to different servers while NVlink connects multiple GPUs within a single server.

Memory Usage

GPU usage is also affected by the training data memory requirements. To give an example, the algorithms having any medical imagery or long videos as their training data set need GPUs with more memory. In comparison, the basic training data sets will work efficiently with cloud GPUs having less memory.

Machine Learning Libraries

One needs to be aware of the different libraries used by various GPUs, and specific Machine Learning Libraries are supportive of some specific GPUs only. Thus, the choice of GPU highly depends on the Machine Learning Libraries in use.

The NVIDIA GPU supports almost all the basic frameworks and MLLs like PyTorch and TensorFlow.

Performance of GPU

The model’s performance is also a factor looked for in selecting a GPU like the basic GPUs are utilized for debugging and development purposes. On the other hand, strong GPUs are utilized to speed up the training time and reduce the number of waiting hours.

Data Parallelism

GPU selection also depends on the size of data being used for processing. If the data set is vast, the GPU should be capable of working on multiple GPU training.

In case the data set is more extensive than usual, the GPU should be able to enable distributed training, and it is so because, in this case, servers are required by the data sets for effective and speedy communication.

Some of the Ideal Deep Learning GPUs for Data Centers and Big Projects

Here are a few GPUs that work best for large-scale AI projects:

NVIDIA Tesla V100

NVIDIA Tesla V100 is a GPU that Tensor Core enables for the operations of machine learning, high-performance computing, and deep learning.

The technology used in it is the NVIDIA Volta, which supports the tensor flow technology used for speeding up the deep leaning tensor operations. The Tesla V100 is known for delivering a 4096-bit memory bus, 149 teraflops of performance, and 32 GB of memory.

NVIDIA Tesla K80

The NVIDIA Kepler Architecture forms the base of the Tesla K80, and this GPU is used for speeding the data analytics and scientific computing tasks. This GPU is inclusive of GPU boost and 4,992 NVIDIA CUDA cores.

The Tesla K80 can deliver 480 GB memory bandwidth, 8.73 teraflops of performance, and 24 GB of GDDR5 memory.

NVIDIA Tesla A100

Multi-instance GPU technology and tensor core form up the NVIDIA Tesla A100, which was designed for the operations of HPC, deep learning, and machine learning.

This GPU has the scalability of thousands of units and can be divided into 7 GPU instances depending on the workload.

The Tesla A100 is capable of delivering 1,555 GB memory bandwidth, 624 teraflops performance, 600GB/s interconnects, and 40GB memory.

Google TPU

Google’s Tensor Processing Units have slightly different working and purposes. The TPUs are ASICs that are cloud-based and used for deep learning. TPUs are available only on the Google Cloud Platform and used with TensorFlow.

Google TPUs can deliver 128 GB high bandwidth memory and 420 teraflops of performance, and its pod versions have a 2D toroidal mesh network, 100 petaflops of performance, and 32TB HBM.

NVIDIA Tesla P100

NVIDIA Pascal Architecture forms the NVIDIA Tesla P100, designed for deep learning and HPC operations. The NVIDIA Tesla P100 delivers a 4,096-bit memory bus, 21 teraflops of performance, and 16GB of memory.

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Wrapping Up

The progress of deep learning operations requires high computational power, and when compared with CPUs, GPUs deliver better processing power, parallelism, and memory bandwidth. Hence, GPUs are ideal for machine learning and deep learning tasks.

Also Read: What is Public Cloud


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