Spark is a powerful, scalable, real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel, GPU clusters is fast becoming the default way to quickly develop and train deep learning models. As data science teams and data savvy companies mature, they'll need to invest in both platforms if they intend to leverage both big data and artificial intelligence for competitive advantage. We'll discuss and show in action an examination of TensorflowOnSpark, CaffeOnSpark, DeepLearning4J, IBM's SystemML, and Intel's BigDL and distributed versions of various deep learning frameworks, namely TensorFlow, Caffe, and Torch.
Today database performance records are being shattered by new innovative ways of tackling big data problems. We're calling it "fast data" and we're leveraging the power of GPUs to query 40 billion dataset rows in just milliseconds. Thanks to a collaboration between MapD, Bitfusion, IBM Cloud and NVIDIA no data problem is too big or complex to process. Using Bitfusion's Boost software, MapD was able to leverage over 64 NVIDIA Tesla GPUs across 16 IBM Cloud servers to filter and aggregate multi-billion row datasets in just milliseconds. Seeing is believing. Come find out why GPUs are quickly becoming the engine for the next generation of enterprise computing applications.