End-to-end Computation on the GPU with a GPU Data Frame

A revolution is occurring across the GPU software stack, driven by the disruptive performance gains GPUs have seen generation after generation. The modern field of deep learning would have not been possible without GPUs, and as a database we are often seeing two-or-more orders of magnitude performance gains compared to CPU systems - but for all of the innovation occurring in the GPU software ecosystem, the systems and platforms themselves still remain isolated from each other. Even though the individual components are seeing significant acceleration from running on the GPU, they must intercommunicate over the relatively thin straw of the PCIe and then through CPU memory. 

In this session, Bill Maimone, VP of Engineering at MapD, will make a case for the open source community to enable efficient intra-GPU communication between different processes running on the GPUs. He will discuss how this integration will allow developers to build new functions to cluster or perform analysis on queries, and will make seamless workflows that combine data processing, machine learning (ML), and visualization possible without ever needing to leave the GPU.