felipe_aramburu

7 ms·

1.
by felipe_aramburu 6mo ago
How does this relate to cuCascade https://github.com/nvidia/cucascade
2.
100TB benchmarks for GPU native data processing on 5TB of RAM(voltrondata.com)
32 points by felipe_aramburu 3y ago|1 comments
3.
52-fold speedup on the US' fastest supercomputer to accelerate Covid-19 research(olcf.ornl.gov)
4 points by felipe_aramburu 6y ago|0 comments
4.
BlazingSQL Speeds Up Queries on Summit Supercomputer with GPU-Accelerated SQL(olcf.ornl.gov)
35 points by felipe_aramburu 6y ago|0 comments
5.
by felipe_aramburu 7y ago
This is actually going to be pretty sick for us at BlazingSQL. We are freaking frothing at the chance to be able to scale beyond system and gpu memory without having to shove data through pipes that are no longer needed. RDMA will be a nece
6.
by felipe_aramburu 7y ago
Have you compared performance between your suggested solutions and what can be achieved using hardware vendor platforms? If not then whats kind of pathetic is how quickly you dismiss the people above who say the HAVE done this before. If yo
7.
by felipe_aramburu 7y ago
When you say SQL format do you mean being able to read the output of a jdbc or odbc driver? If this is the case then mostly just time. You are not the first person to ask about this and now that there are java bindings in cudf this might b
8.
by felipe_aramburu 7y ago
Well it pretty much always was a part of the eco system it just was not open source. We have been contributors to rapids for a while. And yes, we are betting on Nvidia for sure. For most people building GP GPU solutions they are going to ha
9.
by felipe_aramburu 7y ago
Thanks Leo! We love having you all as early adopters of our tech!
10.
by felipe_aramburu 7y ago
Nvidia loves and cherishes you (I think I don't work there). They want you to be able to do this on your laptop, your server, your super computer. If it has been a few years I would encourage you to get your feet wet again because supp
11.
by felipe_aramburu 7y ago
GPU memory is expensive but a big as #@$% computer is even more expensive. When we show comparisons to things like spark we are doing so use cost basis. So if we say something like we are x times faster than this technology on this workload
12.
by felipe_aramburu 7y ago
The collab link below let's you use a gpu for free on Google cloud
13.
by felipe_aramburu 7y ago
Thats a great question. The answer is two-fold. Early on when we first started playing around with General Processing on GPU's we had Nvidia cards to begin with and I started looking at the apis that were available to me. The CUDA ones
14.
by felipe_aramburu 7y ago
This is a Distributed SQL engine not a database. We store no data. You store your data in HDFS, S3, posix, NFS etc. We allow you to query directly from these filesystems of the file formats you have already. You can look here to see the fil
15.
by felipe_aramburu 7y ago
Blazingsql is built on top of CUDF. We are contributors to rapidsai
16.
by felipe_aramburu 7y ago
No. We hadn't heard of what PartiQL was before you wrote that message. At least I hadn't. I am the CTO of blazingsql
17.
by felipe_aramburu 7y ago
Yes to be more specific it works on cuda 9.2 and 10.0 at the moment like the rest of the rapidsai eco system.
18.
Run GPU-Accelerated BlazingSQL 100x Faster Than Apache Spark on Google Colab(blog.blazingdb.com)
17 points by felipe_aramburu 7y ago|1 comments
19.
by felipe_aramburu 8y ago
Why not? That would be in line with what these physicians were doing which was benign actions whose intent was to alleviate suffering. It seems that the greater point is that instead of reacting to mental illness by telling people,"you
20.
by felipe_aramburu 8y ago
Ok. Right now we are in tunnel vision mode to get our distributed version out by GTC in mid march. We will benchmark against clickhouse sometime in March. Do you know of any benchmark tests that are a bit more involved in terms of query com
21.
by felipe_aramburu 8y ago
I am ignorant of clickhouse. It doesn't really compete in the workloads we are interested in. Sorry you feel this way but we are a small team and need to consider tools that integrate with Apache Arrow and CUDF natively. If it doesn&#x
22.
by felipe_aramburu 8y ago
Coming in the current sprint. We have a PR open agaisnt CUDF that brings string support to many of the CUDF algorithms. We will be using this branch in the meantime in our engine while we wait for the PR to be accepted.
23.
by felipe_aramburu 8y ago
What makes it hard to work in a cloud environment? The on demand nature of it? The fact that you only pay for the time you are using it? The ease of scaling or deployment? It would be helpful if you could qualify some of these statements.
24.
by felipe_aramburu 8y ago
And you think https://tech.marksblogg.com/billion-nyc-taxi-rides-clickhous... for example is something that can be considered fast? It takes the user 55 minutes just to load its data into a state so that it can be "que
25.
by felipe_aramburu 8y ago
PG-Strom is a great way to make postgres faster. Nice. But if the purpose is here to build very fast and responsive system which is interacting with other gpu tools well then snap I don't see how it can compete. The old school way of,
26.
by felipe_aramburu 8y ago
Cacaw! I am glad the crowing reached you :). You probably did not get far if you are still on the old numbers that was in the first paragraph. ETL can be very extensive. For example, we first built this when we had to take data from 15 diff
27.
by felipe_aramburu 8y ago
https://github.com/rapidsai is the source for the rapids tools. BlazingSQL itself is not open source though it is free to use.
28.
by felipe_aramburu 8y ago
I do not disagree with you. We are going to be publishing much larger benchmarks soon. Before we could start building BlazingSQL we had to work on CUDF in the rapids eco system. There was alot of work that went into building all of the prim
29.
by felipe_aramburu 8y ago
In lots of ways. AresDB has ingestion. Users take their data and say here make a table from this, persist that data the way I want you to great now I can query it and go along my merry business. I see lots of nice fancy optimizations in the
30.
by felipe_aramburu 8y ago
The purpose of blazing is to accelerate GPU workloads. SQL is just a language we provide users as an interface for performing complex transformations in the rapids eco system. rapids.ai We aren't targeting enterprise sql users specific

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