쿠다

수학노트
둘러보기로 가기 검색하러 가기

노트

위키데이터

말뭉치

  1. R337 드라이버 이후부터는 Geforce 제품군에서의 CUDA 기반 비디오 인코딩/디코딩 라이브러리가 삭제되었다.[1]
  2. CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs).[2]
  3. The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications.[2]
  4. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.[2]
  5. The NVIDIA® CUDA® Toolkit provides a development environment for creating high performance GPU-accelerated applications.[3]
  6. On systems with NVIDIA® Ampere GPUs (CUDA architecture 8.0) or newer, kernels are JIT-compiled from PTX and TensorFlow can take over 30 minutes to start up.[4]
  7. Packages do not contain PTX code except for the latest supported CUDA® architecture; therefore, TensorFlow fails to load on older GPUs when CUDA_FORCE_PTX_JIT=1 is set.[4]
  8. CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units).[5]
  9. CUDA competitor OpenCL was launched by Apple and the Khronos Group in 2009, in an attempt to provide a standard for heterogeneous computing that was not limited to Intel/AMD CPUs with Nvidia GPUs.[5]
  10. CUDA has improved and broadened its scope over the years, more or less in lockstep with improved Nvidia GPUs.[5]
  11. As of CUDA version 9.2, using multiple P100 server GPUs, you can realize up to 50x performance improvements over CPUs.[5]
  12. FreeImage is no longer distributed with the CUDA Samples.[6]
  13. API introduced with CUDA 11 in Ampere chip family tensor cores.[6]
  14. (e8m7) GEMM computation using the __nv_bfloat16 WMMA API introduced with CUDA 11 in Ampere chip family tensor cores.[6]
  15. GEMM computation using the tf32 WMMA API introduced with CUDA 11 in Ampere chip family tensor cores.[6]
  16. The CUDA platform is designed to work with programming languages such as C, C++, and Fortran.[7]
  17. CUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source).[7]
  18. The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux.[7]
  19. Shared memory – CUDA exposes a fast shared memory region that can be shared among threads.[7]
  20. In mid 2009, PGI and NVIDIA cooperated to develop CUDA Fortran.[8]
  21. CUDA Fortran includes a Fortran 2003 compiler and tool chain for programming NVIDIA GPUs using Fortran.[8]
  22. A CUDA programmer is required to partition the program into coarse grain blocks that can be executed in parallel.[8]
  23. When called from the host Fortran program, CUDA Fortran defined subroutines execute in parallel on the GPU.[8]
  24. The OpenCV CUDA module is a set of classes and functions to utilize CUDA computational capabilities.[9]
  25. The OpenCV CUDA module includes utility functions, low-level vision primitives, and high-level algorithms.[9]
  26. The CUDA module is designed as a host-level API.[9]
  27. The OpenCV CUDA module is designed for ease of use and does not require any knowledge of CUDA.[9]
  28. supports CUDA installation for Ubuntu 14.04.3 and RHEL 7.5 on PowerNV (Non-Virtualized) for both diskful and diskless nodes.[10]
  29. To run CUDA 11.1 containers, ensure that your system has drivers 450.80.02 (or higher) installed.[11]
  30. CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for any parallelizable part of a computation.[12]
  31. CUDA is a parallel computing platform and application programming interface model created by Nvidia Corporation.[13]
  32. CUDA creates a path between our computer hardware and our deep learning model.[13]
  33. The NVIDIA CUDA Toolkit provides a development environment for creating high-performance GPU accelerated applications like developing a computer vision model or object detection model.[13]
  34. With the help of CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems.[13]
  35. CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs).[14]
  36. CUDA dramatically speeds up computing applications by using the processing power of GPUs.[14]
  37. CUDA is used by TensorFlow benchmarks.[14]
  38. JCuda has been updated for CUDA 8.0.44 (final).[15]
  39. It offers Java bindings for cuDNN, the NVIDIA CUDA Deep Neural Network library.[15]
  40. Starting with CUDA 7.0, the CUDA toolkit does no longer support developing on 32bit Windows- or Linux platforms.[15]
  41. See the CUDA Toolkit Release Notes for details.[15]
  42. CUDA is available on the clusters supporting GPUs.[16]
  43. You can use module spider cuda to view available modules for a given machine.[16]
  44. To load the default version of CUDA module, use module load cuda .[16]
  45. To select a particular software version, use module load cuda/version .[16]
  46. The CUDA library provides a direct, general purpose C-like SPMD programming model for NVIDIA graphics cards (G8x series onwards).[17]
  47. This library provides bindings to both the CUDA Driver and Runtime APIs.[17]
  48. Due to an interaction between GHC-8 and unified virtual address spaces in CUDA, this package does not currently work with GHCi on ghc-8.0.1 (compiled programs should work).[17]
  49. NVIDIA CUDA enables GPUs programming in a variation of the C programming language.[18]
  50. To get the full advantage of NVIDIA GPUs, you need to use the CUDA parallel computing platform and programming toolkit.[19]
  51. To get the full advantage of NVIDIA GPUs, you need to use NVIDIA CUDA, which is a general purpose parallel computing platform and programming model for NVIDIA GPUs.[19]
  52. To simplify installation of NVIDIA CUDA Toolkit on SUSE Linux Enterprise for High Performance Computing (SLE HPC) 15, we have included a new SUSE Module, NVIDIA Compute Module 15.[19]
  53. This Module adds the NVIDIA CUDA network repository to your SLE HPC system.[19]
  54. CUDA C allows to define C functions, called kernels, that, when called, are executed N times in parallel by N different CUDA threads.[20]
  55. CUDA threads may access data from multiple memory spaces during their execution.[20]
  56. The CUDA programming model assumes that the CUDA threads execute on a physically separate device that operates as a coprocessor to the host running the C program.[20]
  57. The CUDA programming model assumes that both the host and the device maintain their own separate memory spaces in DRAM, referred to as host memory and device memory, respectively.[20]
  58. The following explains how to install CUDA Toolkit 7.5 on 64-bit Ubuntu 14.04 Linux.[21]
  59. The instruction assumes you have the necessary CUDA compatible hardware support.[21]
  60. If everything goes well, you should be able to verify your CUDA installation by running the deviceQuery sample.[21]
  61. It is reasonable to think of CUDA as a set of libraries and associated C, C++, and Fortran compilers that enable you to write code for GPUs.[22]
  62. Here we show a simple example of how to use the CUDA C/C++ language compiler, nvcc , and run code created with it.[22]
  63. To learn more about how the above program works and how to make the use of a GPUs parallelism see CUDA tutorial.[22]
  64. A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals.[23]
  65. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation.[23]
  66. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues.[23]
  67. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems.[23]
  68. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU.[24]
  69. However, CUDA itself can be difficult to learn without extensive programming experience.[24]
  70. The book makes complex CUDA concepts easy to understand for anyone with knowledge of basic software development with exercises designed to be both readable and high-performance.[24]
  71. This document assumes a basic familiarity with CUDA.[25]
  72. if compiling in 64-bit mode; otherwise, pass e.g. -L/usr/local/cuda/lib .[25]
  73. (Clang detects that you’re compiling CUDA code by noticing that your filename ends with .cu .[25]
  74. Invoking clang for CUDA compilation works similarly to compiling regular C++.[25]
  75. GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs.[26]
  76. However, you must make sure that your NVIDIA driver and CUDA toolkit versions are compatible.[27]
  77. yum-utils Select a driver repository for the CUDA Toolkit and add it to your VM.[27]
  78. This command installs CUDA 11.[27]
  79. sudo yum -y install cuda-drivers SLES Connect to the VM where you want to install the driver.[27]

소스

  1. ↑ CUDA
  2. ↑ 2.0 2.1 2.2 CUDA Zone
  3. ↑ CUDA Toolkit
  4. ↑ 4.0 4.1 GPU support
  5. ↑ 5.0 5.1 5.2 5.3 What is CUDA? Parallel programming for GPUs
  6. ↑ 6.0 6.1 6.2 6.3 NVIDIA/cuda-samples: Samples for CUDA Developers which demonstrates features in CUDA Toolkit
  7. ↑ 7.0 7.1 7.2 7.3 Wikipedia
  8. ↑ 8.0 8.1 8.2 8.3 CUDA Fortran Compiler Information
  9. ↑ 9.0 9.1 9.2 9.3 OpenCV: CUDA Module Introduction
  10. ↑ NVIDIA CUDA — xCAT 2.16.1 documentation
  11. ↑ AWS Marketplace: CUDA
  12. ↑ GIGABYTE Global
  13. ↑ 13.0 13.1 13.2 13.3 Cuda Installation on Windows(2020)-Step by Step Process
  14. ↑ 14.0 14.1 14.2 Installing NVIDIA CUDA
  15. ↑ 15.0 15.1 15.2 15.3 Java bindings for CUDA
  16. ↑ 16.0 16.1 16.2 16.3 Ohio Supercomputer Center
  17. ↑ 17.0 17.1 17.2 cuda: FFI binding to the CUDA interface for programming NVIDIA GPUs
  18. ↑ CUDA-accelerated Molecular Modeling Applications
  19. ↑ 19.0 19.1 19.2 19.3 Simplified access to the NVIDIA CUDA toolkit on SUSE Linux for HPC
  20. ↑ 20.0 20.1 20.2 20.3 Scaling Machine Learning: Top 5 Resources to Learn CUDA
  21. ↑ 21.0 21.1 21.2 Installing CUDA Toolkit 7.5 on Ubuntu 14.04 Linux
  22. ↑ 22.0 22.1 22.2 CC Doc
  23. ↑ 23.0 23.1 23.2 23.3 CUDA Programming
  24. ↑ 24.0 24.1 24.2 Professional CUDA C Programming
  25. ↑ 25.0 25.1 25.2 25.3 Compiling CUDA with clang — LLVM 12 documentation
  26. ↑ GPU Parallel Program Development Using CUDA
  27. ↑ 27.0 27.1 27.2 27.3 Installing GPU drivers

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LEMMA': 'CUDA'}]
  • [{'LOWER': 'compute'}, {'LOWER': 'unified'}, {'LOWER': 'device'}, {'LEMMA': 'architecture'}]
  • [{'LOWER': 'nvidia'}, {'LEMMA': 'CUDA'}]
  • [{'LOWER': 'nvidia'}, {'LOWER': 'compute'}, {'LOWER': 'unified'}, {'LOWER': 'device'}, {'LEMMA': 'architecture'}]
  • [{'LOWER': 'nvidia'}, {'LEMMA': 'CUDA'}]
  • [{'LOWER': 'nvidia'}, {'LOWER': 'compute'}, {'LOWER': 'unified'}, {'LOWER': 'device'}, {'LEMMA': 'architecture'}]
  • [{'LEMMA': 'Cuda'}]
  • [{'LOWER': 'nvidia'}, {'LEMMA': 'Cuda'}]
  • [{'LOWER': 'nvidia'}, {'LEMMA': 'Cuda'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=쿠다&oldid=51338"