We’re very happy to announce that with today’s release of version 3.0 of the MapD Analytics Platform we're bringing GPU-accelerated analytics onto distributed clusters!
The MapD Immerse visual analytics client has a core feature we refer to as crossfilter, which allows a filter applied to one chart to simultaneously be applied to the rest of the charts on a dashboard.
Today I’m proud to announce that MapD Technologies has secured $25M in funding in a Series B round lead by New Enterprise Associates (NEA) with participation from NVIDIA, Vanedge Capital, and Verizon Ventures.
Organizations are visualizing and exploring data in ways we once only associated with science fiction films.
We felt it wasn’t fair that only features in our major releases were getting the limelight, so this will be the first in a series of short blog posts featuring an interesting feature or improvement in our regular minor releases of MapD’s GPU-accelerated Core database and Immerse visualization software.
Back when we started the current incarnation of the MapD Core database, we wrote our own parser (written using flex and GNU bison), semantic analysis and optimizer.
Continuing where we left off in our earlier post on MapD 2.0’s Immerse visualization client, today we want to walk you through some of version 2.0’s major improvements to our GPU-accelerated Core database and Iris Rendering Engine.
The taxi dataset is one of the most popular on our site and for good reason, it is not often that you can get behind the wheel of a supercomputer for free.
While 2016 was the year of the GPU for a number of reasons, the truth of the matter is that outside of some core disciplines (deep learning, virtual reality, autonomous vehicles) the reasons why you would use GPUs for general purpose computing applications remain somewhat unclear.