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Comment by sxp | Hacker News Reader

One of the big data points missing from this article is the price. Unless you need features specific to the high end cards such as unlocked 64bit or 16bit performance or antialiased lines, the consumer cards have much higher performancep dollar$ [1]. It would be really interesting if they compared their 8 K80s ~(8 * ($4K for 8TFLOPS)) against a set of GTX 1080s ~($650 for 8TFLOPS)

[1] https://www.youtube.com/watch?v=LC_sx6A5Wko & http://www.videocardbenchmark.net/gpu.php?gpu=Tesla+C2050

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Hi SXP, thanks for your comment. You might want to check out Mark Litwintschik's posts (independent blogger who has benchmarked this dataset across many different databases) for performance on GeForce GTX TITAN X's. 4 x GeForce GTX TITAN X: http://tech.marksblogg.com/billion-nyc-taxi-rides-nvidia-tit.... 8 x K80s: http://tech.marksblogg.com/billion-nyc-taxi-rides-nvidia-tes.... He has additional posts on MapD on Pascal Titan X's and AWS as well. In full disclosure I work at MapD...

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.

Blooper Fixed :)

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.

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

Yep, sorry about that:

Here is the Titan X link http://bit.ly/2e6C3Gg

Here is the K80 link: http://bit.ly/2eiIwvp

It's the memory size here that's likely important, 8 GTX 1080s have a third of the total global memory size as 8 K80s. And the aggregate memory bandwidth is way more important than flops, since almost certainly this workload isn't bound by arithmetic throughput.

One more thing to note is that gamer cards aren't meant to be abused like workstation cards are. You can leave a K80 running at full blast for a week doing stuff, but the same workload will significantly reduce the lifespan of a high-end gamer video card. They're meant for gaming sessions that last a few hours with some outliers going for maybe a full day, but not much more than that.

If you really have a tight budget and need to use gamer cards as workstation cards (e.g. a two-person startup that needs to crunch things on 4 GPUs), find yourself some aftermarket coolers, preferably liquid cooling.

Almost irrelevant, because MapD software charges dwarf the price of the hardware.

https://aws.amazon.com/marketplace/pp/B01M0ZY2OV?qid=1475606...

There's probably a good reason they are using server hardware. But sure, just like you could slap consumer CPUs into a server for a cheaper unit cost, you could use consumer GPUs.

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.

Why are anti-aliased lines specifically a high-end feature? I thought that anti-aliasing was done by over-sampling and then down-sampling, so all drawing primitives would work with it uniformly.

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.

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.

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

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