Note: The following post was originally published on June 23, 2015 on Nvidia's Parallel ForAll blog. This is only an excerpt, you can find the full original here.
Co-written by Alex Suhan and Todd Mostak of MapD.
At MapD our goal is to build the world’s fastest big data analytics and visualization platform that enables lag-free interactive exploration of multi-billion row datasets. MapD supports standard SQL queries as well as a visualization API that maps OpenGL primitives onto SQL result sets.
Although MapD is fast running on x86-64 CPUs, our real advantage stems from our ability to leverage the massive parallelism and memory bandwidth of GPUs. The most powerful GPU currently available is the NVIDIA Tesla K80 Accelerator, with up to 8.74 teraflops of compute performance and nearly 500 GB/sec of memory bandwidth. By supporting up to eight of these cards per server we see orders-of-magnitude better performance on standard data analytics tasks, enabling a user to visually filter and aggregate billions of rows in tens of milliseconds, all without indexing. The following video shows the MapD dashboard, showing 750 million tweets animated in real time. Nothing in this demo is pre-computed or canned. Our big data visual analytics platform is running on 8 NVIDIA Tesla K40 GPUs on a single server to power the dashboard.
Fast hardware is only half of the story, so at MapD we have invested heavily into optimizing our code such that a wide range of analytic workloads run optimally on GPUs. In particular, we have worked hard so that common SQL analytic operations, such as filtering (WHERE) and GROUP BY, run as fast as possible. One of the biggest payoffs in this regard has been moving from the query interpreter that we used in our prototype to a JIT (Just-In-Time) compilation framework built on LLVM. LLVM allows us to transform query plans into architecture-independent intermediate code (LLVM IR) and then use any of the LLVM architecture-specific “backends” to compile that IR code for the needed target, such as NVIDIA GPUs, x64 CPUs, and ARM CPUs.
To continue reading this post, please follow this link to the Nvidia blog where it was originally posted.