Karthikeyan Profile
Karthikeyan

@lxkarthi

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Data Analytics CUDA library Developer @RAPIDSai, @NVIDIA, Deep Learning, VLSI EDA, Parallel computing. My thoughts are my own.

Austin, TX, USA
Joined March 2009
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@lxkarthi
Karthikeyan
2 months
RT @NVIDIAAIDev: 🕔 Don’t let big data slow you down. Learn how to accelerate ⚡ Polars using lazy evaluation and GPU acceleration with NVID….
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@lxkarthi
Karthikeyan
7 months
⚡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 ➡️
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@lxkarthi
Karthikeyan
9 months
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 ➡️
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@lxkarthi
Karthikeyan
9 months
RT @ylecun: Giving a talk at IIT Madras on October 22.
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@lxkarthi
Karthikeyan
9 months
RT @GautamGambhir: A true gem of Mother India! .#RatanTata
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@lxkarthi
Karthikeyan
9 months
RT @NVIDIAHPCDev: Join us at our #AIsummit for an in-depth session on "CUDA Key Features and Beyond" as NVIDIA's Stephen Jones, #CUDA Archi….
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@lxkarthi
Karthikeyan
10 months
📣@DataPolars GPU engine powered by RAPIDSai cuDF is now available in open beta. ⚡Accelerate Polars workflows up to 13x on NVIDIA GPUs.
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@lxkarthi
Karthikeyan
10 months
Exciting to see GPU acceleration added to Polars! Process 100s of millions of rows of data in seconds on NVIDIA GPUs. 🙌 🔢 ⚡(via @rapidsai)
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@lxkarthi
Karthikeyan
11 months
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: Read the blog:
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@lxkarthi
Karthikeyan
1 year
Encoding and Compression Guide for Parquet String Data Using @RAPIDSai @nvidia .
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@lxkarthi
Karthikeyan
1 year
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.
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@lxkarthi
Karthikeyan
1 year
RT @databricks: Jensen Huang, Founder and CEO of @nvidia joined Databricks Co-founder and CEO @alighodsi for a fireside chat on innovation….
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@lxkarthi
Karthikeyan
1 year
RT @NVIDIAHPCDev: Hear Bradley Dice, Senior Software Engineer in GPU-Accelerated Data Analytics at NVIDIA, discuss the effort to hack 'impo….
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@lxkarthi
Karthikeyan
1 year
Learn how to achieve speed-of-(de)light by Revamping llm.c with the #CUDA C++ Core Libraries (CCCL) during this CUDA Mode talk.
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@lxkarthi
Karthikeyan
1 year
RT @blelbach: If anyone going to @cppnow is driving to Denver today or is stranded at the airport, send me a DM or email. Please retweet f….
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@lxkarthi
Karthikeyan
1 year
RT @zstats: Awesome to see @RAPIDSai 24.04 officially launches cuVS (the library to accelerate vector search on GPU), in addition to pandas….
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@lxkarthi
Karthikeyan
1 year
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.
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@lxkarthi
Karthikeyan
1 year
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.
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@lxkarthi
Karthikeyan
1 year
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.
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@lxkarthi
Karthikeyan
1 year
Watch this #GTC24 on-demand session to learn how you can leverage GPU acceleration in your #datascience tools for dataframes, machine learning, graph analytics, vector databases, and more.
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