tmostak

6 ms·

1. 31.▲
   GPU-accelerated ML using SQL(heavy.ai)

   1 points by tmostak 3y ago|0 comments
2. 32.▲
   Interactive ML in a GPU-accelerated database(heavy.ai)

   4 points by tmostak 3y ago|0 comments
3. 33.▲
   by tmostak 5y ago

   You could try OmniSci, it’s a database, rendering engine, and interactive analytics frontend (or any combination of the above) and can easily query and render millions to tens of billions of points interactively while allowing for things li
4. 34.▲
   Making geospatial joins interactive at scale(omnisci.com)

   38 points by tmostak 5y ago|2 comments
5. 35.▲
   by tmostak 6y ago

   OmniSci can run on any Nvidia GPU with sufficient RAM (we'd generally recommend >= 8GB), including a 3080. (I have two 3090s myself!) It also can run purely (and performantly) on CPU, and with the Intel's help we're furthe
6. 36.▲
   Faster data I/O from OmniSci with Apache Arrow(omnisci.com)

   3 points by tmostak 6y ago|0 comments
7. 37.▲
   by tmostak 6y ago

   Definitely can pick it up with a HT, just caught it for ~3 minutes in the Bay Area on a Baofeng HT with whip antenna, and even picked up the first part while I was still indoors. There was a lot of static although I could make out some of t
8. 38.▲
   by tmostak 6y ago

   Unfortunately it's probably not in the cards in the near term just do to other priorities and insufficient demand (plus alternatives like HIP for AMD). I will say a lot of us here at OmniSci would kill to leverage the latent GPUs in ou
9. 39.▲
   by tmostak 6y ago

   Hi @nikita, good to reconnect. When you say an array and not a hash table, do you just mean a simple perfect hash table indexed by the offset of the dictionary id? We use this fairly extensively for inputs of bounded domain (i.e. dictionary
10. 40.▲
    by tmostak 6y ago

    For those wanting to try it for themselves, we recently released a preview of our full stack for Mac (containing both OmniSciDB as well as our Immerse frontend for interactive visual analytics), available for free here: https://w
11. 41.▲
    by tmostak 6y ago

    Just to clarify, most of the query engine is built around LLVM-based JIT compilation, and CUDA is not really used per say except for GPU-specific operators like atomic aggregates and thread synchronization, and of course we use the driver A
12. 42.▲
    by tmostak 6y ago

    To be fair, the c5d.9xlarge instances are $1.728 each per hour, or $5.18 for the 3-server cluster (looks to be about $3.06/hr for reserved 1-year pricing). Even with reserved pricing, that's $26,806 a year, or 6.5X more than a $4K
13. 43.▲
    by tmostak 6y ago

    Mark did a benchmark of SQLite using its internal file format a few years ago ( https://tech.marksblogg.com/billion-nyc-taxi-rides-sqlite-pa... ), clocking the import at 5.5 hours. It looks like this was done though on a spin
14. 44.▲
    1.1B Taxi Rides Using OmniSciDB and a MacBook Pro(tech.marksblogg.com)

    205 points by tmostak 6y ago|57 comments
15. 45.▲
    Interactively query 1B+ records on a Mac with OmniSci(omnisci.com)

    9 points by tmostak 6y ago|0 comments
16. 46.▲
    The GPU Database Evolves into an Analytics Platform(nextplatform.com)

    4 points by tmostak 6y ago|0 comments
17. 47.▲
    Explore the impact of 120B opioid pills from the DEA's ARCOS database(omnisci.com)

    3 points by tmostak 7y ago|0 comments
18. 48.▲
    Interactively visualizing 1.7 billion stars from the Gaia dataset(omnisci.com)

    2 points by tmostak 7y ago|1 comments
19. 49.▲
    by tmostak 7y ago

    If you sign up for a cloud trial, we can provide you with an ODBC interface. Other options are using our JDBC or our Python/JS interfaces, which are in our open source: https://github.com/omnisci .
20. 50.▲
    by tmostak 7y ago

    Hi John, thanks! One option is our cloud (pricing is on the page). https://www.omnisci.com/cloud . You can also spin us up from the AWS, Azure, or GCP marketplaces. If you need an on-prem option, feel free to reach out: inf
21. 51.▲
    by tmostak 7y ago

    Also worth checking out OmniSci, ( https://www.omnisci.com ), formerly known as MapD, which can query and visualize tens of billions of geospatial records in sub-second timeframes. See here for an interactive demo of 11.6 billion
22. 52.▲
    by tmostak 7y ago

    Also worth checking out OmniSci (formerly MapD), which features an LLVM query compiler to gain large speedups executing SQL on both CPU and GPU: https://github.com/omnisci/omniscidb . And here's a link to a blog p
23. 53.▲
    by tmostak 7y ago

    Thanks @jonbaer, and lot's more exciting stuff to come!
24. 54.▲
    by tmostak 7y ago

    Check out this example crunching 1.45 billion rows on my 32GB Macbook Pro (CPU-only). https://twitter.com/ToddMostak/status/1162067442081751040?s=... With a GPU cluster it's very possible to handle 100+ billi
25. 55.▲
    by tmostak 7y ago

    Thanks! We're big Observable fans, and you can find quite a few examples here: https://observablehq.com/search?query=mapd and https://observablehq.com/search?query=omnisci . But imagine we could do some
26. 56.▲
    Bridging analytics and data science workflows with GPUs(omnisci.com)

    19 points by tmostak 7y ago|11 comments
27. 57.▲
    by tmostak 7y ago

    OmniSci transparently caches data across the memory of the CPUs and GPUs on a server, so after the initial read, it is likely that the data for subsequent queries will be in memory. We've also optimized our storage formats and multithr
28. 58.▲
    by tmostak 7y ago

    This interactive and cross-filterable visualization of ~12 billion AIS records may be of interest: https://www.omnisci.com/demos/ships/#/dashboard/1
29. 59.▲
    by tmostak 8y ago

    LLVM actually has a native PTX backend, NVPTX, but since the Nvidia ISA is proprietary we use the CUDA driver API to generate the binary for the target GPU arch. (see here in MapD where it generates the PTX from the LLVM IR: https:/&#
30. 60.▲
    by tmostak 8y ago

    Elias Stehle's work out of Technical University of Munich is pretty awesome, see here: https://dl.acm.org/citation.cfm?id=3064043 .