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Cuda Libraries

Raj Garg ME17B016

Math Libraries: For compute-intensive applications in areas such as molecular dy- namics, computational fluid dynamics, etc.

  • cuBLAS: GPU-accelerated basic linear algebra (BLAS) library
  • cuFFT: GPU-accelerated library for Fast Fourier Transforms
  • CUDA Math Library : GPU-accelerated standard mathematical function li- brary
  • cuTENSOR: GPU-accelerated tensor linear algebra library

Parallel Algorithm Libraries: For highly efficient parallel algorithms.

  • Thrust : GPU-accelerated library of C++ parallel algorithms and data struc- tures.

Image and Video Libraries: For image and video decoding, encoding, and processing.

  • nvJPEG: High-performance GPU-accelerated library for JPEG decoding
  • NVIDIA Performance Primitives: Provides GPU-accelerated image, video, and signal processing functions.
  • NVIDIA Video Codec SDK: A complete set of APIs, samples, and docu- mentation for hardware-accelerated video encode and decode on Windows and Linux.
  • NVIDIA Optical Flow SDK: Exposes the latest hardware capability of NVIDIA Turing™GPUs dedicated to computing the relative motion of pixels between im- ages.

Communication Libraries: Performance-optimized multi-GPU and multi-node com- munication primitives.

  • NVSHMEM : OpenSHMEM standard for GPU memory, with extensions for improved performance on GPUs.
  • NCCL : Open-source library for fast multi-GPU, multi-node communications that maximizes bandwidth while maintaining low latency.

Deep Learning Libraries: For Deep Learning applications.

  • NVIDIA cuDNN: GPU-accelerated library of primitives for deep neural net- works.
  • NVIDIA TensorRT™ : High-performance deep learning inference optimizer and runtime for production deployment.
  • NVIDIA Jarvis: Platform for developing engaging and contextual AI-powered conversation apps.
  • NVIDIA DeepStream SDK : Real-time streaming analytics toolkit for AI- based video understanding and multi-sensor processing.
  • NVIDIA DALI : Portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications.

Partner Libraries: OpenCV, FFmpeg, ArrayFire, MAGMA, etc.

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