{"id":7451,"date":"2017-06-21T07:00:18","date_gmt":"2017-06-21T15:00:18","guid":{"rendered":"http:\/\/formtek.com\/blog\/?p=7451"},"modified":"2017-05-09T07:55:42","modified_gmt":"2017-05-09T15:55:42","slug":"artificial-intelligence-creation-of-the-gpu-open-analytics-initiative-gaoi","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/artificial-intelligence-creation-of-the-gpu-open-analytics-initiative-gaoi\/","title":{"rendered":"Artificial Intelligence: Creation of the GPU Open Analytics Initiative (GAOI)"},"content":{"rendered":"

GPU (Graphic Processing Units) are specialized computer chips that are revolutionizing the area of artificial intelligence (AI). \u00a0Their original purpose may have been for speeding up graphic displays on monitors, but applications of GPU chips have become far reaching. \u00a0GPU chips are equipped with thousands of core processors, compared to the 2-4 cores that are available in standard CPUs. \u00a0Because there are so many core processors, GPUs enable many different tasks that can be worked on in parallel.<\/p>\n

Because many or most AI algorithms can be written to be solved as\u00a0parallel problems, GPUs are able to much more quickly attack and solve AI problems, compared to standard CPUs. \u00a0Machine learning, deep learning, self-driving vehicles, facial recognition, data analytics, are examples of some of the many AI problems that are best suited to be solved by GPUs.<\/p>\n

Recently a new organization formed to advance the study of data science and create common data framework on GPUs. \u00a0The new organization is called\u00a0the GPU Open Analytics Initiative<\/a>. \u00a0The group has already made available a GPU Data Frame for improving the interfacing with GPUs.<\/p>\n

Todd Mostak<\/a>, CEO and co-founder of MapD Technologies, said that<\/a> “the data science and analytics communities are rapidly adopting GPU computing for machine learning and deep learning. However, CPU-based systems still handle tasks like subsetting and preprocessing training data, which creates a significant bottleneck. The GPU Data Frame makes it easy to run everything from ingestion to preprocessing to training and visualization directly on the GPU. This efficient data interchange will improve performance, encouraging development of ever more sophisticated GPU-based applications.”<\/p>\n

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GPU (Graphic Processing Units) are specialized computer chips that are revolutionizing the area of artificial intelligence (AI). \u00a0Their original purpose may have been for speeding up graphic displays on monitors, but applications of GPU chips have become far reaching. \u00a0GPU…<\/span><\/p>\n

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