Kumaran babu Kaliamoorthy’s Post

GSoC 2024: Compile GPU kernels using ClangIR https://lnkd.in/edRMsW3H #cpp #cplusplus

Runtime Fatbin Creation Using the NVIDIA CUDA Toolkit 12.4 Compiler https://lnkd.in/ehKEFH9R #cpp #cplusplus

Semiconductor suppliers offer MCUs/MPUs with Neural Processor Units (NPU) coprocessors capable of improving Machine Learning processing performance. This article shows how an imaging classification application greatly benefits from using the NPU and Machine Learning model optimization tools like NVIDIA's TAO (Train Adapt Optimize).

Simplifying AI Development with Mojo and MAX Current Generative AI applications struggle with complex, multi-language workloads across various hardware types. The Modular Mojo language and MAX platform offer a solution by unifying CPU and GPU programming into a single Pythonic model. This approach aims to simplify development, boost productivity, and accelerate AI innovation. Presented by Chris Lattner, co-founder and CEO of Modular, at the AI Engineer World's Fair in San Francisco. Check it out: https://lnkd.in/dQxT9ejY #Mojo #Python #PyTorch #MAX #Modular

🚀 𝐏𝐨𝐥𝐚𝐫𝐬 𝐆𝐏𝐔 𝐄𝐧𝐠𝐢𝐧𝐞 𝐢𝐬 𝐇𝐞𝐫𝐞! 𝐏𝐨𝐥𝐚𝐫𝐬, the blazing-fast DataFrame library, got even faster with its new GPU engine (powered by 𝐑𝐀𝐏𝐈𝐃𝐒 𝐜𝐮𝐃𝐅) in v1.3! 🔥 Key highlights: ✅ Process 10-100+ GB data interactively on a single GPU ✅ Simple integration - just add engine="𝐠𝐩𝐮" to collect() ✅ Seamless fallback to CPU for unsupported operations ✅ Built right into the Polars Lazy API I tried it out and the performance boost is incredible! The speed difference compared to traditional DataFrame operations is mind-blowing. 🤯 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐧𝐨𝐭𝐞𝐛𝐨𝐨𝐤: https://lnkd.in/dkqB7j6E Want to dive deeper? Check out this video: https://lnkd.in/dqzjBVNq by Krish Naik #DataScience #GPU #Programming #Python #DataEngineering #NVIDIA #Tech

Compose services can define GPU device reservations if the Docker host contains such devices and the Docker Daemon is set accordingly. To allow access only to GPU-0 and GPU-3 devices: services: test: image: tensorflow/tensorflow:latest-gpu command: python -c "import tensorflow as tf;tf.test.gpu_device_name()" deploy: resources: reservations: devices: - driver: nvidia device_ids: ['0', '3'] capabilities: [gpu]

✍️Lightening up the 'darkness' - PyTorch 2.5.0 : '..excited to announce the release of PyTorch® 2.5! This release features a new CuDNN backend for SDPA, enabling speedups by default for users of SDPA on H100s or newer GPUs. As well, regional compilation of torch.compile offers a way to reduce the cold start up time for torch.compile by allowing users to compile a repeated nn.Module (e.g. a transformer layer in LLM) without recompilations. Finally, TorchInductor CPP backend offers solid performance speedup with numerous enhancements like FP16 support, CPP wrapper, AOT-Inductor mode, and max-autotune mode. This release is composed of 4095 commits from 504 contributors since PyTorch 2.4. We want to sincerely thank ...' - Extract #github #coldstart #pytorch #speedups #commits #skdscans

Tip and Tricks to correct a Cuda Toolkit installation in Conda https://lnkd.in/d3bVV_j7

A high performance sorting library for Javascript. 70x speedup when sorting ints and floats. #NVIDIA GPU with CUDA Compute Capability (5.0 or higher) sortIntegers: let array = new Int32Array([3, 1, 2]); let buffer = Buffer.from(array.buffer); AccelSort.sortIntegers(buffer, array.length); sortFloat: let array = new Float32Array([5.8, -10.7, 1507.6563, 1.0001]); let buffer = Buffer.from(array.buffer); AccelSort.sortFloats(buffer, array.length);