Graphics Processing Units

The world we live in has multiple colours, each contained within a billion pixels when viewed through a photograph or video. The vibrance of nature around us is impeccable when captured in the camera and processing these images takes an even greater amount of resources.

Every image has a lot of data contained within it. Each pixel corresponds to a value. Furthermore, when these images combine as multiple frames to create a video, the bar even goes higher. The millions of frame that make up a video contain immense amount of data and tasks like processing and rendering such videos provides an even greater challenge.

To tackle this challenge of computing millions of pixels simultaneously, the Graphics Processing Unit architecture was proposed. The graphics processing units play a major role in the processing and rendering of such data. GPUs have essentially become an integral part of computing in today’s world.

We cannot imagine any high computing task without the use of the GPUs that we know today. They can perform complex calculations and implement parallelism using technologies like CUDA by NVIDIA.

The GPUs have been in the market for a long time now and have found various applications in today’s world. They are used from basic tasks like 3D modelling and video rendering to heavy and compute intensive tasks like training deep convolutional neural networks and transformers with petabytes of image data.

Nowadays, graphics cards are extensively used for blockchain mining for mining cryptocurrency resources like Bitcoins and Ethereum. In blockchain mining, the graphics processing unit has a major role to play as it verifies each and every hash that passes through the mining algorithm. There are are millions of smaller transactions within each hash and to receive a substantial amount of resource as a reward, the GPU needs to process all the transactions within a hash and it is only after that hash is completed, the cryptocurrency reward is given to the miner.

Graphics Processing Units are also used in different workloads in the Artificial Intelligence domain like Machine Learning and Deep Learning tasks. Different computer vision tasks make use of GPUs for training models as without a GPU it may take a lot of time to train the model. The advantage of using a GPU in this scenario is that the GPUs provide the capability of providing the data to different layers of the neural network simultaneously which makes the process much faster without adding the overhead of scheduling tasks like in a normal CPU. In the newer GPUs like NVIDIA, the CUDA technology is used. The CUDA technology implements parallel processing which makes processing faster as memory is assigned in a coalesced fashion where each memory block can be simultaneously accessed and the processing can take place. The NVIDIA DGX has been designed specifically while keeping in mind different machine learning and deep learning tasks to make the training of AI models that take high resolution images as input, faster.

Another application of GPUs in the real world scenario is handling and processing BIG Data or processing Data Science workflows. Big data needs a large amount of computational power that is provided by the GPUs.

While GPUs are extremely useful and resourceful in multiple tasks, they also have some disadvantages like multitasking. Essentially, GPUs are not made to multitask. They can process data parallely within a single task but while handling multiple tasks at once, a GPU may face a hard time.