TIL : NVidia's nvcomp now supports Zstandard compression and decompression in GPU : github.com/NVIDIA/nvcomp/…

Ibis Nice, this uses the Matplotlib Ibis integration. Ibis also drops into HoloViews - here's another talk that shows an example with HEAVY.AI (formerly Omnisci) brighttalk.com/webcast/14525/…

This is a great time to plug the Voltron Data 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.

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

How? Use cuml pairwise_distances and warm it up docs.rapids.ai/api/cuml/stabl…

Inside this blog, we share a talk from @gilforsyth at #PyData NYC that breakdowns Ibis and demos how to get the power of #python interacting with optimized database engines: voltrondata.com/resources/ibis…

📢 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…

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

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…

Our CEO, Josh Patterson (Josh Patterson) talks using #OpenStandards to augment data systems on the latest episode of Imply's podcast. Get his take on ApacheArrow Ibis DuckDB and the importance of connectivity in the #DataAnalytics ecosystem. bit.ly/3HyrlnI

Modularity and compatibility is the future of data systems. If you’re interested in RAPIDS AI Ibis Apache Calcite ApacheArrow or substrait.io and at VLDB 2025 🇬🇧, you should check out the CDMS talks. Voltron Data will have several there and presenting 2 talks!

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…

Another gift brought to you by the RAPIDS AI team…

Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT | NVIDIA Technical Blog bit.ly/3EAxC1d

The collective work on the #ComposableCodex is 🔥! Voltron Data is stronger than the sum of its parts. Modular composable accelerated systems start w/ ApacheArrow RAPIDS AI & more; the future is connected/now. Building bridges not walls! Can’t wait for the next chapters to drop.

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

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…

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