12 posts found under,

GPU database

Since starting work on MapD more than five years ago while taking a database course at MIT, I had always dreamed of making the project open source. It is thus with great pleasure to announce that today our company is open sourcing the MapD Core database and associated visualization libraries, effective immediately. The code is available on Github under an Apache 2.0 license. It has everything you need to build a fully functional installation of the MapD Core database, enabl... read more

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! We’ve been hard at work for months to extend the unique advantages of our SQL-compliant GPU database from being able to run on one server to now being able to scale across multiple servers, allowing our customers to take on even larger datasets while still maintaining the fluid, instant data exploration experi... read more

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. This provides a natural interface for data exploration, allowing a multi-dimensional view of data even as a user drills deep into a dataset. From a technical perspective, crossfiltering is not difficult (on the surface). Behind each Immerse chart is a SQL statement. When an e... read more

Organizations are visualizing and exploring data in ways we once only associated with science fiction films. Analysts live a world with access to a plethora of data visualization and reporting tools. Long gone are the days of Excel charting as the primary means for visualizing data. As the toolkit has evolved, the amount of data we collect and analyze has exploded. Websites and phone apps track a user’s every click or swipe. IoT devices record the location of every vehi... read more

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. This approach offers the most control since everything in the pipeline can be adjusted to the unique needs of our system, but we've realized that our main strength lies in the actual execution of the queries. In the context of the limited resources of a startup, we have to pick our battles. We soon faced a dilemma... read more

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. Before we delve into the details, main themes for this release are: speed, robustness, and further visual analytics power. Our system is able to steadily deliver extremely fast query speeds across a larger set of SQL queries and when analyzing dat... read more

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. Still, without direction, it can be hard to uncover the insights in the data that often give our audiences a rush. With that in mind we will be creating a series of these “cheatsheets” to help you grasp the power of speed at scale. Each post will talk about how to interact with the GPU-powered relational database (MapD... read more

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. As a company whose products are tuned for this exceptional compute platform, we have a tendency to assume familiarity, often incorrectly. Our New Year’s resolution is to explain, in language desig... read more

After many months of hard work, refinement and improvement, we’re very happy to announce the release of version 2.0 of the MapD Core database and Immerse visual analytics platform. This is the result of a valuable collaboration with our users and customers since our public launch in March, and is designed to give them even more of the analytical power, ease of use and GPU-accelerated speed that they’ve become used to in our first version. Today we want to walk you through so... read more

As we enter the final stretch of our summer, it is time to start looking ahead to the conference-rich third and fourth quarters. After a period of relative calm, we are back with a vengeance, starting almost immediately. While you can always get a demo, sometimes in-person works best. Here is the list of where to find us: Siggraph: Our love for visualization is well known. You see it in the product, in the demos we make and the blog posts we write. We are delighted to h... read more

There is little question that the GPU age is upon us. We see it everywhere, from game consoles to supercomputers and now the datacenter, GPUs are permeating more and more of the computing ecosystem. By boasting order of magnitude performance improvements on key tasks and exhibiting massive cost of ownership advancements these once specialized chips are writing a new chapter in enterprise computing. The early lines of that chapter have been written by pioneers like Nvidia... read more

MapD was built from the ground up to enable fully interactive querying and visualization on multi-billion row datasets. An important feature of our system is the ability to visualize large results sets, regardless of their cardinality. Ordinary BI systems do fine when rendering standard bar or pie charts but often fall over when required to render the millions of records often associated with scatter plots, network graphs and various forms of geovisualization. Being able to... read more