Provided by: nvidia-cuda-dev_7.5.18-0ubuntu1_amd64 bug

NAME - The NVIDIA CUDA Driver Library - The NVIDIA CUDA Runtime Library - The NVIDIA cuBLAS Library - The NVIDIA cuSPARSE Library - The NVIDIA cuSOLVER Library, - The NVIDIA cuFFT Libraries - The NVIDIA cuRAND Library,, - The NVIDIA CUDA NPP Libraries - The NVIDIA NVVM Library - The NVIDIA libdevice Library, - The NVIDIA CUINJ Libraries - The NVIDIA Tools Extension Library

       The CUDA Driver API library for low-level CUDA programming.
       The  CUDA  Runtime  API library for high-level CUDA programming, on top of the CUDA Driver
       The cuBLAS library is an implementation of BLAS (Basic Linear Algebra Subprograms) on  top
       of  the  NVIDIA  CUDA runtime. It allows the user to access the computational resources of
       NVIDIA Graphics Processing Unit (GPU), but does not auto-parallelize across multiple GPUs.

       To use the cuBLAS library, the application must allocate the required matrices and vectors
       in  the  GPU  memory  space,  fill  them  with  data,  call the sequence of desired cuBLAS
       functions, and then upload the results from the GPU memory space back  to  the  host.  The
       cuBLAS  library  also  provides  helper functions for writing and retrieving data from the
       The cuSPARSE library contains a set of basic linear algebra subroutines used for  handling
       sparse matrices. It is implemented on top of the NVIDIA CUDA runtime (which is part of the
       CUDA Toolkit) and is designed to be called from C and C++. The  library  routines  can  be
       classified into four categories:

       *  Level 1: operations between a vector in sparse format and a vector in dense format

       *  Level 2: operations between a matrix in sparse format and a vector in dense format

       *   Level  3:  operations  between a matrix in sparse format and a set of vectors in dense
       format (which can also usually be viewed as a dense tall matrix)

       *  Conversion: operations that allow conversion between different matrix formats
       The cuSOLVER library contains LAPACK-like functions in dense and  sparse  linear  algebra,
       including linear solver, least-square solver and eigenvalue solver.,
       The  NVIDIA  CUDA Fast Fourier Transform (FFT) product consists of two separate libraries:
       cuFFT and cuFFTW. The cuFFT library is designed to  provide  high  performance  on  NVIDIA
       GPUs.  The  cuFFTW  library  is  provided as porting tool to enable users of FFTW to start
       using NVIDIA GPUs with a minimum amount of effort.

       The FFT is a divide-and-conquer  algorithm  for  efficiently  computing  discrete  Fourier
       transforms of complex or real-valued data sets. It is one of the most important and widely
       used numerical algorithms in computational physics  and  general  signal  processing.  The
       cuFFT  library  provides  a  simple  interface  for computing FFTs on an NVIDIA GPU, which
       allows users to quickly leverage the floating-point power and parallelism of the GPU in  a
       highly optimized and tested FFT library.
       The  cuRAND  library provides facilities that focus on the simple and efficient generation
       of high-quality pseudorandom and quasirandom numbers. A pseudorandom sequence  of  numbers
       satisfies  most  of the statistical properties of a truly random sequence but is generated
       by a deterministic algorithm. A quasirandom sequence of n-dimensional points is  generated
       by a deterministic algorithm designed to fill an n-dimensional space evenly.,,
       NVIDIA  NPP  is  a  library  of  functions for performing CUDA accelerated processing. The
       initial set of functionality in the library focuses on imaging and video processing and is
       widely  applicable  for  developers in these areas. NPP will evolve over time to encompass
       more of the compute heavy tasks in a variety  of  problem  domains.  The  NPP  library  is
       written to maximize flexibility, while maintaining high performance.

       NPP can be used in one of two ways:

       *   A  stand-alone  library  for  adding  GPU  acceleration to an application with minimal
       effort. Using this route allows developers to add GPU acceleration to  their  applications
       in a matter of hours.

       *  A cooperative library for interoperating with a developer’s GPU code efficiently.

       Either  route  allows  developers to harness the massive compute resources of NVIDIA GPUs,
       while simultaneously reducing development times.
       The NVVM library is used by NVCC to compile CUDA binary code to run on NVIDIA GPUs.
       The libdevice library is a collection of NVVM  bitcode  functions  that  implement  common
       functions   for  NVIDIA  GPU  devices,  including  math  primitives  and  bit-manipulation
       functions. These functions  are  optimized  for  particular  GPU  architectures,  and  are
       intended to be linked with an NVVM IR module during compilation to PTX.,
       The CUDA internal libraries for profiling. Used by nvprof and the Visual Profiler.
       The NVIDIA Tools Extension Library.


       cuda-binaries(1), cuda-gdb(1)


       For     more     information,     please     see     the     online    documentation    at


       ©2013 NVIDIA Corporation. All rights reserved.