Using GPUs to Speed Through the 1.2B Record Taxi Dataset

Nice demo, but it would be even nicer if you could get the blooper fixed: "Mapbox's Openstreetmap"

MapBox is an active and respected participant in the OpenStreetMap project and uses our data in some of its products, but that is it.

Both those links got shortened for some reason, into invalid ones.

Thanks for the info. The tl;dr is "It's fantastic to see that I've been able to use a machine that costs 1/10th of the one used in the 8 x Tesla K80s benchmark but still have queries running within 33% of the previous performances witnessed."

However, I'm suspicious of the numbers in those articles since the author lists only 4 data points in each trial and doesn't mention the stdev in his measurements. One of his measurements was .964 vs .891 so it looks like the Titan Xs were 90% as fast as K80s if the numbers can be trusted.

For Graphistry's GPU platform, we suggest our users to go with server-grade GPUs because they get (a) more memory and (b) great multitenancy. So using MapD as a personal system is an expensive use of resources, but when a system is architected and billed as an elastic, multitenant system, total cost of ownership for a team is less. Not all platforms are built for this (and I don't know enough about MapD vs. other GPU databases), but that's the engineering view.

And mini-disclaimer: Graphistry is a related platform focused on scaling & automating investigations. Part of that is a GPU compute stack the we started building around the same time as MapD, though we're not in the database business. E.g., our customers will generally use us to look across multiple other systems that already feature high-availability, long-term storage, and scaleout querying for TB+ storage. As some examples: SQL engines, Spark, Splunk, Datastax, and various graph databases.

Thats one way of doing anti-aliasing (MSAA), there are many others.

High quality anti-aliased lines aren't a bottleneck in video games, but they're very important in CAD applications, so enterprise customers paid a large premium for Quadro cards with CAD-specific functionality. The specific list of premium vs consumer features has varied over time, but previous generations of consumer cards could have their professional features unlocked via modded drivers.

The question, though, is what are you oversampling? Depending on how the line gets rasterized, supersampling (or multisampling) may or may not help you at all.

Thanks for the tip, I found this table/d3 implemenation but it looks like they use it only for the grouping table: https://github.com/d8d/mapd-sources/blob/master/out/home/jen...

Why don't you publish the real data then?

Source: https://www.mapd.com/product/

It tops off at 192GB (8 x 24GB). Assuming in a couple of gens (#slots & gpu mem) will put it in 1TB range.

Hi infinite8s, to get additional information on how that chart was made, you can to go https://www.mapd.com/product/ scroll down to the bar chart, and click “See Details” under the chart. Shows the machines used, queries, and the source data set and size. Note that the machine configurations used to generate the chart were normalized for equivalent cost on AWS, i.e. the chart is hardware-dollar normalized.

thanks, but at 5 bucks an hour for an entry-level instance (single 12GB GPU) I'm looking at 120 bucks a day if I don't want to constantly re-upload my dataset into MapD (a very slow operation judging by Mark Litwintschik's posts linked by you). That's a very very high price for such a modest hardware configuration, not to mention the more credible one which goes for an eye-watering 30 bucks an hour ie not much change from a grand a day. Not for us startup folk, clearly.

I have to say it seems your pricing for such a new entrant and before having built share, is bound to attract very stiff newcomer competition. "Interesting" business model.