NAME

       libcuda.so - The NVIDIA CUDA Driver Library

       libcudart.so - The NVIDIA CUDA Runtime Library

       libcublas.so - The NVIDIA cuBLAS Library

       libcusparse.so - The NVIDIA cuSPARSE Library

       libcusolver.so - The NVIDIA cuSOLVER Library

       libcufft.so, libcufftw.so - The NVIDIA cuFFT Libraries

       libcurand.so - The NVIDIA cuRAND Library

       libnppc.so, libnppi.so, libnpps.so - The NVIDIA CUDA NPP Libraries

       libnvvm.so - The NVIDIA NVVM Library

       libdevice.so - The NVIDIA libdevice Library

       libcuinj32.so, libcuinj64.so - The NVIDIA CUINJ Libraries

       libnvToolsExt.so - The NVIDIA Tools Extension Library

DESCRIPTION

   libcuda.so
       The CUDA Driver API library for low-level CUDA programming.

   libcudart.so
       The CUDA Runtime API library for high-level CUDA programming, on top of the CUDA Driver API.

   libcublas.so
       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 GPU.

   libcusparse.so
       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

   libcusolver.so
       The  cuSOLVER library contains LAPACK-like functions in dense and sparse linear algebra, including linear
       solver, least-square solver and eigenvalue solver.

   libcufft.so, libcufftw.so
       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.

   libcurand.so
       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.

   libnppc.so, libnppi.so, libnpps.so
       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.

   libnvvm.so
       The NVVM library is used by NVCC to compile CUDA binary code to run on NVIDIA GPUs.

   libdevice.so
       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.

   libcuinj32.so, libcuinj64.so
       The CUDA internal libraries for profiling. Used by nvprof and the Visual Profiler.

   libnvToolsExt.so
       The NVIDIA Tools Extension Library.

SEE ALSO

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

NOTES

       For more information, please see the online documentation at http://docs.nvidia.com/cuda/index.html.

COPYRIGHT

       ©2013 NVIDIA Corporation. All rights reserved.