This year, revenues for big data and business analytics solutions will reach $189.1 billion, according to IDC.
Managing big data can be a huge challenge with traditional software tools as they are not specifically designed to handle massive amounts of data. The insights to be gained from big data can unlock huge business value. Businesses should be able to extract these insights quickly from their data to respond quickly to the needs of the market.
As a hypothetical example, a restaurant chain should be able to quickly analyze the customer data that can help them to predict what sort of menu items they should sell to maximize profitability while predicting the inventory requirements in realtime.
Traditional CPUs don’t have enough parallel compute path bandwidth to deliver instantaneous results with massive volumes of data to deliver insights.
GPUs have large number of simpler ( compared to traditional CPU cores) processor cores along with faster access onboard RAM that can deliver massive speedups over traditional general purpose CPUs. Tesla V100 GPU, the most advanced GPU from Nvidia can deliver 900 GB/Sec. This kind of bandwidth is necessary to obtain faster results from extremely large datasets (that possibly consists of billions of rows of data) in a matter of seconds and minutes.
Querying the information is just a part of the big picture; as you query billions of lines/documents, the results often would be millions of lines; and, visualizing the data that the queries have returned is essential to make business decisions. Again, GPUs are necessary in the visualization process as the traditional use case of a GPU is visual/graphics rendering.
According to Gartner, augmented analytics is the next wave of disruption in the data and analytics market. Augmented analytics uses machine learning and natural language processing to automate analytics and insights from data. As big data and business analytics are knit together, building skills and expertise in the augmented analytics space is going to be the key.
For running machine learning workloads — and its subset deep learning — the massive parallel processing abilities provided by GPUs are necessary. GPUs decrease the time required for deep learning training and inferencing, enabling organizations to accomplish results quickly. Also, GPUs provide lower cost per computation than CPUs, whose high overall costs make them unviable for machine learning workloads.
In the Indian AI ecosystem, various startups and SMEs are innovating with machine learning based technologies like natural language processing (NLP), image generation, and object recognition in images and videos. Indian VCs like Pi Ventures specifically focus on funding startups in the AI & machine learning space. At E2E Networks, we have recognized the need for presenting a cost-effective GPU Cloud solution in the Indian market, and we recently launched Tesla V100 based GPU instances with 32 GB onboard RAM, with a massive pricing advantage over any other public cloud offering comparable GPU compute infrastructure.
Whether its weather forecasting, predicting the disease risk factors before symptoms show up, or detecting fraud in millions of financial transactions in real-time, crunching massive amounts of data is essential; for which, the massive parallel-processing and high bandwidth memory capabilities of GPUs are indispensable.
Article By
Mr Tarun Dua Managing Director and Co-Founder E2E Networks
