GPU Open Analytics Initiative @gpuoai
GOAI strives to create common data frameworks enabling developers and statistical researchers to accelerate data science on GPUs. gpuopenanalytics.com Joined July 2017-
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Want to speed up your Pandas code by 10-1000x? With no code change? The folks from @nvidia have created cuDF pandas accelerator mode. By using this line in Jupyter, you automatically leverage your GPU to run Pandas code: %load_ext cudf.pandas From command-line: python -m cudf.pandas yourscript.py (It also accelerates 3rd party libraries that leverage Pandas.) Check out this demo notebook: nvda.ws/3rNqETS I've had a chance to use the pre-release version and am very impressed!
100% pandas coverage at up to 150x with 0 code change. Introducing cudf.pandas - for instantly accelerating your current code on #NVIDIA #GPUs. #DataScience . docs.rapids.ai/api/cudf/stabl…
RAPIDS cuDF’s pandas accelerator mode is now available in open beta, accelerating #pandas nearly 150x with zero code changes. #DataScience, #Python, #RAPIDS Read the announcement blog: 👇 nvda.ws/469JnqC
Want to leverage #GPUs and optimize workflows for #analytics and/or #MachineLearning? We set out to see if we could achieve both throughput AND high compression ratio without compromising one or the other..…and we did. Read this post to learn how! voltrondata.com/resources/gpus…
#OpenSource gives you more choices. #Standards help you make better choices. This is a key theme in The #ComposableCodex. “For data systems developers, open source was a lightbulb moment… it meant that engineering teams were not spread so thin trying to innovate across the entire surface area of the data system. Instead, they could focus on innovating in targeted, high-impact areas. Second, it expanded their choices…[but] the downside of having more choices is that ultimately you do have to make choices. An evergreen problem with open source is: how do you choose? How do you choose which user interface, query optimizer, or database connector to build with and depend on? That is where standards come in.” Lots of choices. A good one to make now: Read The Codex Chapter 00 “A New Frontier”! bit.ly/codex00 #ComposableDataSystems #OpenStandards #OSS
The collective work on the #ComposableCodex is 🔥! @VoltronData is stronger than the sum of its parts. Modular composable accelerated systems start w/ @ApacheArrow @RAPIDSai & more; the future is connected/now. Building bridges not walls! Can’t wait for the next chapters to drop.
🚀 Introducing: The Composable Codex! This is a 5-part dive into #ComposableDataSystems. Starting from zero: A New Frontier. It’s a look at the history, technology, trends, and opportunities that are all critical to our arrival at the Composable era. bit.ly/codex00
Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT | NVIDIA Technical Blog bit.ly/3EAxC1d
Another gift brought to you by the @RAPIDSai team…
There's an amazingly convenient way to install the *full* NVIDIA CUDA dev stack on Linux, that I've never seen mentioned before. It's all done with conda! I just tried it and it worked perfectly.🧵 docs.nvidia.com/cuda/cuda-inst…
There's an amazingly convenient way to install the *full* NVIDIA CUDA dev stack on Linux, that I've never seen mentioned before. It's all done with conda! I just tried it and it worked perfectly.🧵 docs.nvidia.com/cuda/cuda-inst…
Modularity and compatibility is the future of data systems. If you’re interested in @RAPIDSai @IbisData @ApacheCalcite @ApacheArrow or @substrait_io and at @VLDBconf, you should check out the CDMS talks. @VoltronData will have several there and presenting 2 talks!
We’re excited to present at #VLDB2023 and the co-located Composable Data Management Systems (CDMS) workshop this week! This is where #database innovation + industry best practice meet. Preview what we'll cover, and catch our talks if you attend! bit.ly/VLDB2023 @VLDBconf
Our CEO, Josh Patterson (@datametrician) talks using #OpenStandards to augment data systems on the latest episode of @implydata's podcast. Get his take on @ApacheArrow @IbisData @duckdb and the importance of connectivity in the #DataAnalytics ecosystem. bit.ly/3HyrlnI
Preview release of the nanoarrow-based @SnowflakeDB Connector for #Python is available! This connector is ~10x smaller in size and removes a hard dependency on a specific version of #PyArrow. Learn how the nanoarrow integration makes it possible. medium.com/snowflake/supe…
DGX GH200: Nvidia ties 256 Grace-Hopper Superchips by 36 NVLink Switches to provide >1 EF FP8 (or ~9PF of FP64) o 144TB unified memory o 900 GB/s GPU-to-GPU bandwidth o 128 TB/s bisection bandwidth o End-of-year availability hpcwire.com/2023/05/28/nvi… #HPC #AI via @HPCwire
@HPCwire To illustrate the potential speedups, @nvidia shared internal benchmarking projections, showing improvements from 2.2x (for the 1T GPT3) all the way to 6.3x (for the 40TB Distributed Join) #HPC #AI #DGX_GH200
📢 CuPy v13.0.0a1 & v12.1.0 is here with more enhancements and improved performance. Highlights include expanded coverage of cupyx.scipy.signal and cupyx.scipy.interpolate APIs, faster random number generation, and more. github.com/cupy/cupy/rele…
Inside this blog, we share a talk from @gilforsyth at #PyData NYC that breakdowns @IbisData and demos how to get the power of #python interacting with optimized database engines: voltrondata.com/resources/ibis…
How? Use cuml pairwise_distances and warm it up docs.rapids.ai/api/cuml/stabl…
Everyone is out here deploying complex vector databases for semantic search or thinking they need to scale to billions of queries on day one You can run cosine sim for 150k records (768 emb size) on a single GPU in just ~3ms
This is a great time to plug the @VoltronData summary blog about this. voltrondata.com/resources/spee… reminder a single @nvidia V100 (now 3 generations old) out performs all CPU implementations by over 2.5x and Spark by 20x… again… on a single GPU.
let's try simple sorting of a parquet file but with bigger VM, 16 CPU/60 GB RAM #DuckDB 29.6s #ApacheDatafusion 1min 4s #Apachespark : need to configure java, not interested #Polars 1min 6s #Clickhouse 1min 24s #pyarrow 2m 3s github.com/djouallah/parq…
TIL : NVidia's nvcomp now supports Zstandard compression and decompression in GPU : github.com/NVIDIA/nvcomp/…
To new beginnings! So excited to join @wesmckinn and others to help drive the future of @ApacheArrow! Woot woot! Join us! medium.com/@patterson.jos…
Bojan Tunguz @tunguz
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RAPIDS AI @RAPIDSai
13K Followers 166 Following End to end CUDA accelerated #datascience libraries built on @apachearrow, scaled with @dask_dev & Dask-SQL for ETL, ML, graph analytics, and DL preprocessing
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RAPIDS AI @RAPIDSai
13K Followers 166 Following End to end CUDA accelerated #datascience libraries built on @apachearrow, scaled with @dask_dev & Dask-SQL for ETL, ML, graph analytics, and DL preprocessing
Jacob Tomlinson @_JacobTomlinson
2K Followers 629 Following Building @rapidsai and @dask_dev at @NVIDIA. Tinker with kr8s in my spare time. Views are my own. he/him. 🦋 https://t.co/oxaQdmUkxo
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4K Followers 978 Following VP Solution Architecture and Engineering @NVIDIA; @RAPIDSai; former @PIFgov (#44). Building bridges not walls. Accelerating Data Science.
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485K Followers 3K Following Advisor to startups. Freelancer. @LeadershipData founder. Global Speaker. Top #B2B influencer & social promoter #DataScience #AI #ML. PhD Astrophysics @Caltech
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2.6M Followers 48 Following The official handle for NVIDIA. Blog: https://t.co/JAn5eKOTBT Support: https://t.co/6ln5FVnA2o All our social media: https://t.co/Uc56dL57Dh
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SciPyConf @SciPyConf
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41K Followers 8K Following Put data & AI into action by creating scalable, interactive data apps for your business with Plotly.
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4K Followers 3K Following Director of Technical Marketing @NVIDIA for Enterprise #AI #DL #ML #DeepLearning #MachineLearning #HPC #Innovation #RAPIDS #DevOPs *My opinions are my own!*
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