Graphic Processing UnitGPU's are special purpose processors that are designed to take up intensive programming tasks and thus reduces the strain on the system’s CPUs. By taking the load from CPU, the system’s CPU can process more jobs than it could have without a GPU. In a layman’s language, it’s a piece of hardware made to do complex mathematical calculations.

A GPU is very good at vector matrices, factor operations, floating point operations etc to solve complex programming problems at a higher speed. GPU technology has evolved at a great pace. It was never thought that GPU that are made to process shading triangles in parallel schemes would be able to do everyday tasks and would be able to run data models that will one day support big Artificial Intelligence projects using the same pipelines.

Gaming was one of the early drivers for GPU technology but today GPUs are not only restricted to gaming but are used at a very high level. The transformation has been significant from a Graphic processing Unit to a General processing unit. GPU has accelerated data centers to 20-50 times more, resulting in much faster results and accelerated application development. GPU has reduced a Data Centre’s space by replacing the clustered off shelf servers by rack of GPU servers that acquires comparatively lesser space. For example 5 datacenter racks can be reduced to only few U’s in Single rack with Nvidia HGX Server and for petaFLOPS of tensor operations! Without doubt Nvidia (NVIDIA.IN) tops the list of top 10 GPU manufacturers as published by Robotics and automation news (@roboticsandautomationnews). Few other GPU technology companies that have been leading the league are Advanced Micro Devices (AMD), Asus (@AsusIndia), etc. So the sooner organizations realize the value and importance of GPU technology, they will be better prepared for the Data boom in the coming future.

According to the Forbes Magazine Data center’s storage matters more today than it was yesterday. GPU technology has really scaled up the power of computing by providing the speed and efficiency. GPU ready data centers provide high performance and use less power to handle advance workloads in lesser floor space. In the time when there is too much data over flooding the data centers that can be used for making as well as saving money , GPU servers have become a boon for Data Scientists and Big Data companies in the world where Machine Learning and Data Analytics have advanced at an exponential rate. Today it has become important to have a data center architecture that support workflows for analytics and IoT data and having your GPU on cloud saves you cost and space of setting up an internal Data Centre within your organization. 

Machine Learning capabilities are need of the day in all the applications whether it’s to analyze your customer’s needs or for block chain platforms. GPU servers help to query large amount of Data in milliseconds that too without a cost of setting up supercomputers. According to IDC the data growth is going to increase by 10 fold by 2025. (Refer to the graph below). ‘Big Data market revenues are projected to increase from $42B in 2018 to $103B in 2027” as predicted by Forbes. Forbes also published that '79% of enterprise executives say that companies who will not embrace #BigData will lose market strength & may face extinction.'

How about having instant jumpstart GPU powered environment with all the required components for machine learning & AI, such as Tensorflow, Python, Anaconda, OpenCV, KERAS, PyTorch, Django etc., at GPUONCLOUD and taking advantage of the scalable GPU computes on subscription basis? Give it a go for trials at GPUONCLOUD!


Wednesday, December 12, 2018

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