Karthikeyan @lxkarthi
Data Analytics CUDA library Developer @RAPIDSai, @NVIDIA, Deep Learning, VLSI EDA, Parallel computing. My thoughts are my own. karthikeyann.github.io Austin, TX, USA Joined March 2009-
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Extending Velox - GPU Acceleration with cuDF velox-lib.io/blog/extending…
🕔 Don’t let big data slow you down. Learn how to accelerate ⚡ Polars using lazy evaluation and GPU acceleration with NVIDIA cuDF. ➡️ nvda.ws/3YHQFkP
⚡Accelerate pandas workflows with RAPIDS cuDF. Learn how GPU acceleration and Unified Virtual Memory (UVM) enable faster, scalable data processing, even for datasets exceeding GPU memory. Tech blog ➡️ nvda.ws/4fYEuqn bit.ly/3OGnedr
Join our monthly #CUDA Virtual Connect With Experts event to chat with NVIDIA developers about ETL, cuDF, and cuML, and ask live questions.🌟 📅 October 25, 10-11:30am PT. Learn more ➡️ bit.ly/4fgRo21
Giving a talk at IIT Madras on October 22.
A true gem of Mother India! #RatanTata
Join us at our #AIsummit for an in-depth session on "CUDA Key Features and Beyond" as NVIDIA's Stephen Jones, #CUDA Architect, dives into the latest innovations and future of #GPU computing. 📆 Oct 9, 2:00 PM - 2:40 PM EDT ➡️ nvda.ws/3YdcyIU 📍 Washington, DC
📣@DataPolars GPU engine powered by RAPIDSai cuDF is now available in open beta. ⚡Accelerate Polars workflows up to 13x on NVIDIA GPUs. bit.ly/4dsrUgF
Exciting to see GPU acceleration added to Polars! Process 100s of millions of rows of data in seconds on NVIDIA GPUs. 🙌 🔢 ⚡(via @RAPIDSai) bit.ly/3XCjttE
NEW feature: RAPIDS cuDF supports up to 2.1B rows of text data. Watch #pandas code with large strings get GPU-accelerated up to 30x with zero code changes. Try the notebook: nvda.ws/3LXTerT Read the blog: nvda.ws/3SKkDBD bit.ly/46GM5pq
Encoding and Compression Guide for Parquet String Data Using @RAPIDSai @nvidia developer.nvidia.com/blog/encoding-…
Learn what it takes to do scans at the speed-of-light and how the #CUDA Core Compute Libraries (#CCCL) make it easy to achieve accelerated performance in this latest CUDA MODE talk. dy.si/uuUwyp
Jensen Huang, Founder and CEO of @nvidia joined Databricks Co-founder and CEO @alighodsi for a fireside chat on innovation in the age of AI. "Whatever you do, just start," said Huang. "This is a fast-moving train. You don’t want to wait and observe an exponential trend."
Hear Bradley Dice, Senior Software Engineer in GPU-Accelerated Data Analytics at NVIDIA, discuss the effort to hack 'import pandas' to boost its performance using cuDF, a GPU DataFrame library. Sign up for @pycon's event #PyConUS to hear this talk live: nvda.ws/4dIbhyU.
Learn how to achieve speed-of-(de)light by Revamping llm.c with the #CUDA C++ Core Libraries (CCCL) during this CUDA Mode talk. bit.ly/3JSDxBg
If anyone going to @cppnow is driving to Denver today or is stranded at the airport, send me a DM or email. Please retweet for visibility.
Awesome to see @RAPIDSai 24.04 officially launches cuVS (the library to accelerate vector search on GPU), in addition to pandas 2.0 support, and significant expansions of @networkx_team GPU acceleration! medium.com/rapids-ai/rapi…
NVIDIA Accelerates Inference on Meta Llama 3. A single NVIDIA H200 Tensor Core GPU generated about 3,000 tokens/second — enough to serve about 300 simultaneous users — in an initial test using the version of Llama 3 with 70 billion parameters. blogs.nvidia.com/blog/meta-llam…
Meta Llama 3, the latest open #LLM from Meta — built with NVIDIA technology — is optimized to run on our GPUs from the cloud and data center to the edge and the desktop. bit.ly/3Un6CLb
Discover the enhanced capabilities of @RAPIDSai #cuDF which boosts Pandas performance up to 100x without code changes. Watch the on-demand #GTC24 session to learn how. nvda.ws/4cXeNFm

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