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.

NVIDIA                                             2013-12-25                                  cuda-libraries(7)