HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
bakks
virginian
C2012–2026
bakks / virginian

virginian

A GPU Database

2Distinct people
3Mentions
0Explicit recommenders
151GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A GPU Database

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year3 dated mentions

Observed mentions: 2012-02-16 05:07:31 UTC to 2026-08-09 15:39:02 UTC.

012012-01-01 00:00:00 UTC to 2013-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20122013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20122012-01-01 00:00:00 UTC2013-01-01 00:00:00 UTC110
20132013-01-01 00:00:00 UTC2014-01-01 00:00:00 UTC101
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC000
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC000
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

3 sources
Very cool project! I'm glad to see this area is getting some attention. I did research in GPU databases a couple summers ago at NEC, if anyone is interested the project is at https://github.com/bakks/virginian . Some background mixed with some opinion: This is an area still without a major commercial competitor and there aren't yet any such public product efforts as far as I know (IBM has a couple of researchers publishing on the topic). The difficulty is that really exploiting the GPU requires rethinking architecture from the ground up, including how your query plan handles parallelism, how your data is stored, and most significantly, how to efficiently move data to the GPU for processing. Most databases aren't partitioned nearly well enough to shoehorn GPU processing in on the backend, and it seems like the vast majority of recent datab
Read the rest
ase development has been around highly distributed systems and commodity hardware, eg Hadoop. A German company called ParStream has a DB that uses GPUs for certain indexing operations, though last time I spoke to them it sounded like there is much much more that can be done with GPU hardware. In general, speedup is highly variable, and depends on the data set, indexing characteristics, data type (fixed vs variable length), individual query, etc, but its fairly common to see 10x speedups over optimized multicore query execution. Though the extreme parallelism of the GPU is responsible for some of this, in my opinion the big win there is just absolute memory throughput. Newer Tesla GPUs have a memory throughput of > 100 GB/s without breaking a sweat, while most CPUs are around 30 GB/s. If you can push data through your hardware that quickly, its easy to see how the GPU can have such an advantage. The difficulty, however, is actually moving the data into GPU memory (1-8 GB generally) to exploit this throughput, which is a major bottleneck (some of my research was around doing this better). I understand that GPU direct disk access is just becoming a reality, which could make this easier. The holy grail, however, is a full-powered integrated server CPU/GPU. If both of these components had equivalent main-memory access and this architecture becomes cheap and commoditized, then GPU databases will immediately become extremely relevant. I expect that there will be a serious commercial competitor in this area within 3 years, either in the relational data warehousing space (think Vertica) or closely integrated into MapReduce type execution. My guess as to the reason it doesn't exist is that 1) production DB development is really hard and 2) GPU processing is only a really big win for certain workloads, namely heavy analytical/research workloads over fixed-size data. As I said, if GPUs become commoditized in server hardware or fully integrated with CPUs, however, I expect this domain will quickly become extremely important. This is a very cool area and its common to see 10x speedups even with optimized CPU implementations. Always happy to chat about this stuff, feel free to shoot me a message.
In Fast Database Emerges from MIT Class, GPUs and Student’s Invention

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.