tmostak

12 ms·

91.
Visualizing Large Datasets on the GPU with Vega and MapD(mapd.com)
117 points by tmostak 9y ago|24 comments
92.
by tmostak 9y ago
MapD | San Francisco (city) | MapD Community Manager, Backend Developer, Director of Application Design, Frontend Engineer, Senior Graphics Engineer, other roles (ONSITE/REMOTE) MapD ( https://www.mapd.com ) is an open source
93.
by tmostak 9y ago
MapD | San Francisco (city) | MapD Community Manager, Backend Developer, Director of Application Design, Frontend Engineer, Senior Graphics Engineer, other roles (ONSITE/REMOTE) MapD ( https://www.mapd.com ) is an open source
94.
End-to-end Computation on the GPU with the GPU Data Frame(mapd.com)
1 points by tmostak 9y ago|0 comments
95.
by tmostak 9y ago
Note that MapD is not fully in-memory. It can pull from CPU Memory and SSD as necessary. So its essentially as fast as the fastest disk-based database at its worst and likely two orders-of-magnitude or more faster in many common scenarios w
96.
by tmostak 9y ago
For more context Timothy Prickett Morgan of Next Platform wrote about the Initiative a few days ago: https://www.nextplatform.com/2017/05/09/goai-keeping-databas... .
97.
GOAI: Keeping Databases, Analytics, and Machine Learning All on the GPU(nextplatform.com)
10 points by tmostak 9y ago|0 comments
98.
by tmostak 9y ago
60-100GB? Many of our users run with 8 GPUs and 192GB VRAM, with larger VRAM buffers likely coming soon. And that's before you consider the fact that you don't need to cache all columns and partitions in a column store or compres
99.
by tmostak 9y ago
It's a bot.
100.
by tmostak 9y ago
Yes its definitely alpha functionality now but we're very excited about this piece and potentially supporting more of the Arrow spec. In case you missed it check out the first project we are working on with Continuum and H2O with the &
101.
by tmostak 9y ago
These are relatively old but might be helpful: https://www.mapd.com/blog/2015/06/04/mapd/ and https://www.mapd.com/blog/2015/06/25/mapd-massive-throughput... .
102.
by tmostak 9y ago
GPUs have phenomenal memory bandwidth (multiple terabytes per second in a multi-GPU server) in addition to the computational parallelism they are known for. MapD caches the most recently touched data in the VRAM across multiple GPUs, meanin
103.
by tmostak 9y ago
Yes. :)
104.
by tmostak 9y ago
You might be surprised. Telcos need to troubleshoot network problems in real time, automakers and insurance companies need to track cars in real-time, oil companies need to interactively query and visualize geological data, and the infosec
105.
by tmostak 9y ago
I can very much tell you this was not sponsored.
106.
by tmostak 9y ago
We actually already can import geospatial formats via GDAL (shapefiles, geojson, kml). We can render points and polygon data. More geospatial abilities to come!
107.
by tmostak 9y ago
Yes absolutely. We were part of their launch and are just going through the process now of adding MapD to the marketplace.
108.
by tmostak 9y ago
Its honestly a pretty small dataset for us. MapD can easily do sub-100ms queries on 100B+ records with a single rack. You can fit a lot of GPU + CPU memory on a small cluster. That RAM goes even further when you use compression like you men
109.
1.1B taxi rides benchmarked on distributed GPU-powered MapD(tech.marksblogg.com)
53 points by tmostak 9y ago|22 comments
110.
MapD Raises $25M Series B to Drive Adoption of GPU-Powered Analytics(mapd.com)
4 points by tmostak 10y ago|0 comments
111.
by tmostak 10y ago
MapD | San Francisco (city) | Backend Developer, Frontend Visualization Developer, Technical Marketing Director (ONSITE) MapD ( https://www.mapd.com ) is a Google Ventures/Nvidia/Verizon Ventures/Vanedge/In-Q-T
112.
Using Python for Data Viz with MapD's GPU Core Database(mapd.com)
2 points by tmostak 10y ago|0 comments
113.
by tmostak 10y ago
You can run MapD ( https://www.mapd.com ) and get big speedups over CPU analytic databases ( http://tech.marksblogg.com/benchmarks.html ). We'll be launching on Google Cloud soon.
114.
Using Apache Calcite for Fast and Flexible Query Analysis(mapd.com)
3 points by tmostak 10y ago|0 comments
115.
DB benchmark shootout on 1.2B row NYC taxi dataset(tech.marksblogg.com)
2 points by tmostak 10y ago|0 comments
116.
by tmostak 10y ago
It looks like year is extracted from pickup_datetime at ETL, so hence its not a fair comparison against the other databases that do this at runtime in Q3 and Q4. In something like MapD Q3 would be nearly as fast as Q1 (~20ms) without the ex
117.
MIT Startup Puts GPUs to Work for Big Data Analytics(news.mit.edu)
3 points by tmostak 10y ago|0 comments
118.
by tmostak 10y ago
People in the database space use the term "transactional database" loosely to refer to databases optimized for handling simultaneous inserts, deletes and updates at rates often measured in thousands per second - the kind that you
119.
by tmostak 10y ago
Your first point is an implementation detail. Why couldn't CPU RAM be used as well in a GPU database system? Re CPU vs GPU performance you are neglecting the fact that GPU RAM can be an order-of-magnitude faster than CPU RAM, not to me
120.
by tmostak 10y ago
Not every database is a transactional database. The precise goal of databases like Redshift, Vertica, MemSQL, SAP HANA, Exasol and Impala are to be able to crunch multi-billion row datasets as fast as possible. Indexes often don't he

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