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NVIDIA CUDA Software Development Kit (CUDA SDK)
Release Notes
Version 2.2.1 for Linux
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Please, also refer to the release notes of version 2.2 of the CUDA Toolkit, 
installed by the CUDA Toolkit installer.

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TABLE OF CONTENTS
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I.   Quick Start Installation Instructions
II.  Detailed Installation Instructions
III. Creating Your Own CUDA Program
IV.  Known Issues
V.   Frequently Asked Questions
VI.  Change Log
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I. Quick Start Instructions
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For more detailed instructions, see section II below.

0. a. Install the NVIDIA Linux display driver by executing the file
      NVIDIA-Linux-*-pkg1.run

   (Note this is pkg2 for 64-bit linux.)
   For information on installing NVIDIA Linux display drivers, please refer to 
   the NVIDIA Accelerated Linux Driver Set README and Installation Guide:
   http://us.download.nvidia.com/XFree86/Linux-x86/1.0-9755/README/index.html

1. Install version 2.2 of the NVIDIA CUDA Toolkit by executing the file
   cudatoolkit_2.2-linux_*.run corresponding to your Linux distribution

   Add the CUDA binaries and lib path to your PATH and LD_LIBRARY_PATH 
   environment variables.

2. Install version 2.2.1 of the NVIDIA CUDA SDK by executing the file 
   cudasdk_2.2.1-linux.run

   The installer will prompt you to enter an installation path for the SDK or
   accept the default.  We will refer to the path you choose as 
   SDK_INSTALL_PATH.
   
3. Build the SDK project examples.  

	cd <SDK_INSTALL_PATH>
	make
    
4. Run the examples (32-bit or 64-bit Linux)
    
	cd <SDK_INSTALL_PATH>/bin/linux32/release
        matrixmul
   or
	cd <SDK_INSTALL_PATH>/bin/linux64/release
        matrixmul

   (or any of the other executables in that directory)

See the next section for more details on installing, building, and running
SDK samples.

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II. Detailed Installation Instructions
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This package consists of a ".run" file. This is a self-extracting archive that
decompresses its contents to a temporary folder and then installs the contents 
to a path that you specify.  The archive is:

cudasdk_2.2.1-linux.run : NVIDIA CUDA SDK Installer 

In addition, a NVIDIA Linux Display driver is needed to run CUDA code on an 
NVIDIA GPU.  CUDA 2.2 requires version 185.18.08 Beta or later of the linux 
NVIDIA Display Driver.  Please see the NVIDIA CUDA Toolkit 2.2 release notes 
for more details.

   For information on installing NVIDIA Linux display drivers, please refer to 
   the NVIDIA Accelerated Linux Driver Set README and Installation Guide:
   http://us.download.nvidia.com/XFree86/Linux-x86/1.0-9755/README/index.html

1. Install version 2.2 of the NVIDIA CUDA Toolkit by executing the file
   cudatoolkit_2.2-linux_*.run corresponding to your Linux distribution

   To install, run the cudatoolkit_2.2-linux_*.run script.  You will be prompted
   for the path to where you want to put the CUDA files. In the following we will 
   call this path <CUDA_INSTALL_PATH>. It is recommended that you run the 
   installer as root and use the default install path (/usr/local). 

   Make sure that you add the location of the CUDA binaries (such as nvcc) to 
   your PATH environment variable and the location of the CUDA libraries
   (such as libcuda.so) to your LD_LIBRARY_PATH environment variable.

   In the bash shell, one way to do this is to add the following lines to the 
   file .bash_profile in your home directory. 
        
	PATH=$PATH:<CUDA_INSTALL_PATH>/bin
        LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<CUDA_INSTALL_PATH>/lib
        export PATH
        export LD_LIBRARY_PATH
 
2. Install the NVIDIA CUDA SDK by executing the file cudasdk_2.2.1-linux.run

   To install, run the cudasdk_2.2.1-linux.run script.  You will 
   be prompted for the path to where you want to put the CUDA SDK.  You can 
   regard the CUDA SDK as user code (it is a set of examples), and therefore
   the default installation is in the current user's home directory 
   (~/NVIDIA_CUDA_SDK). You must either accept the default or specify a path
   to which the user has write permissions. 

   We will refer to the path you choose as SDK_INSTALL_PATH below.
   
3. Build the SDK project examples.  
    a. Go to <SDK_INSTALL_PATH> ("cd <SDK_INSTALL_PATH>")
    b. Build:
        - release    configuration by typing "make".
        - debug      configuration by typing "make dbg=1".
        - emurelease configuration by typing "make emu=1".
        - emudebug   configuration by typing "make emu=1 dbg=1".

    Running make at the top level first builds libcutil, a utility library used
    by the SDK examples (libcutil is simply for convenience -- it is not a part
    of CUDA and is not required for your own CUDA programs).  Make then builds
    each of the projects in the SDK.  

    NOTES:
    - The release and debug configurations require a CUDA-capable GPU to run
      properly (see Appendix A.1 of the CUDA Programming Guide for a complete
      list of CUDA-capable GPUs).
    - The emurelease and emudebug configurations run in device emulation mode, 
      and therefore do not require a CUDA-capable GPU to run properly.
    - You can build an individual sample by typing "make" 
      (or "make emu=1", etc.) in that sample's project directory. For example:

        cd <SDK_INSTALL_PATH>/projects/matrixmul
        make emu=1

      And then execute the sample with:
        <SDK_INSTALL_PATH>/bin/linux32/emurelease/matrixmul
   or
        <SDK_INSTALL_PATH>/bin/linux64/emurelease/matrixmul

    - To build just libcutil, type "make" (or "make dbg=1") in the "common" 
      subdirectory:

        cd <SDK_INSTALL_PATH>/common
        make

4. Run the examples from the release, debug, emurelease, or emudebug 
   directories located in 

        /bin/linux32/[release|debug|emurelease|emudebug].
   or 
        /bin/linux64/[release|debug|emurelease|emudebug].


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III. Creating Your Own CUDA Program
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Creating a new CUDA Program using the NVIDIA CUDA SDK infrastructure is easy.
We have provided a "template" project that you can copy and modify to suit your
needs. Just follow these steps:

1. Copy the template project

        cd <SDK_INSTALL_PATH>/projects
        cp -r template <myproject>

2. Edit the filenames of the project to suit your needs

        mv template.cu myproject.cu
        mv template_kernel.cu myproject_kernel.cu
	mv template_gold.cpp myproject_gold.cpp

3. Edit the Makefile and source files.  Just search and replace all occurences 
   of "template" with "myproject".

4. Build the project

        make

   You can build a debug version with "make dbg=1", an emulation version with 
   "make emu=1", and a debug emulation with "make dbg=1 emu=1".

5. Run the program

        ../../bin/linux32/release/myproject
   or
        ../../bin/linux64/release/myproject

   (It should print "Test PASSED")

6. Now modify the code to perform the computation you require.  See the
   CUDA Programming Guide for details of programming in CUDA.


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IV. Known Issues
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Note: Please see the CUDA Toolkit release notes for additional issues.

1. "ld: cannot find -lglut".  On some linux installations (notably default RHEL 
   4 update 3 installations), building the simpleGL example (and other examples 
   that use OpenGL) can result in a linking error like the following.

        /usr/bin/ld: cannot find -lglut

   Typically this is because the SDK makefiles look for libglut.so and not for
   variants of it (like libglut.so.3). To confirm this is the problem, simply 
   run the following command.

        ls /usr/lib | grep glut

   You should see the following (or similar) output.

    lrwxrwxrwx  1 root root     16 Jan  9 14:06 libglut.so.3 -> libglut.so.3.8.0
    -rwxr-xr-x  1 root root 164584 Aug 14  2004 libglut.so.3.8.0

   If you have libglut.so.3 in /usr/lib, simply run the following command 
   as root.

        ln -s /usr/lib/libglut.so.3 /usr/lib/libglut.so

   If you do NOT have libglut.so.3 then you can check whether the glut package
   is installed on your RHEL system with the following command.

        rpm -qa | grep glut

   You should see "freeglut-2.2.2-14" or similar in the output.  If not, you 
   or your system administrator should install the package "freeglut-2.2.2-14".
   Refer to the Red Hat and/or rpm documentation for instructions.

   If you have libglut.so.3 but you do not have write access to /usr/lib, you 
   can also fix the problem by creating the soft link in a directory to which 
   you have write permissions and then add that directory to the libarary 
   search path (-L) in the Makefile.

2. In code sample alignedTypes, the following aligned type does not provide
   maximum throughput because of a compiler bug:
       typedef struct __align__(16) { 
           unsigned int r, g, b; 
       } RGB32;
   The workaround is to use the following type instead:
       typedef struct __align__(16) { 
           unsigned int r, g, b, a; 
       } RGBA32;
   as illustrated in the sample.

3. Some Linux distributions may not include the GLU library. You can download this
   from this search engine, matching the correct Linux distribution.

   http://fr.rpmfind.net/linux/rpm2html/search.php?query=libGLU.so.1&submit=Search+...


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V. Frequently Asked Questions
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The Official CUDA FAQ is available online on the NVIDIA CUDA Forums:
http://forums.nvidia.com/index.php?showtopic=36286

Note: Please also see the CUDA Toolkit release notes for additional Frequently 
Asked Questions.

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VI. Change Log
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Release 2.2.1
* Updated common.mk file to removed -m32 when generating CUBIN output
* Support for PTX output has been added to common.mk
* CUDA Driver API samples: simpleTextureDrv, matrixMulDrv, and threadMigration

  have been updated to reflect changes:

    - Previously when compiling these CUDA SDK samples, gcc would generate a 
      compilation error when building on a 64-bit Linux OS if the 32-bit glibc 
      compatibility libraries were not previously installed.  This SDK release
      addresses this problem.  The CUDA Driver API samples have been modified 
      and solve this problem by casting device pointers correctly before 
      being passed to CUDA kernels.
    - When setting parameters for CUDA kernel functions, the address offset 
      calculation is now properly aligned so that CUDA code and applications
      will be compatible on 32-bit and 64-bit Linux platforms.
    - The new CUDA Driver API samples by default build CUDA kernels with the 
      output as PTX instead of CUBIN.  The CUDA Driver API samples now use 
      PTXJIT to load the CUDA kernels and launch them.
* Added sample pitchLinearTexture that shows how to texture from pitch linear
  memory

Release 2.2 Final
* Added Mandelbrot (Julia Set), deviceQueryDrv, radixSort, SobolQRNG, threadFenceReduction
* CUDA Event Blocking Stream Synchronization (CU_CTX_BLOCKING_SYNC) is now supported on Linux
* New CUDA 2.2 capabilities: 
    - supports zero-mem copy (GT200, MCP79) 
        * simpleZeroCopy SDK sample
    - supports OS allocated pinned memory (write combined memory).  Test this by:
        > bandwidthTest -memory=PINNED -wc

Release 2.1 Final
* CUDA samples that use OpenGL interop now call cudaGLSetGLDevice after the GL context is created.
  This ensures that OpenGL/CUDA interop gets the best possible performance possible.

Release 2.1 Beta
* Added CUDA smokeParticles (volumetric particle shadows samples)
* Note: added cutil_inline.h for CUDA functions as an alternative to using the
        cutil.h macro definitions
* For CUDA samples that use the Driver API, you must install the Linux 32-bit
  compatibility (glibc) binaries on Linux 64-bit Platforms.  See Known issues in about
  section IV on how to do this.

Release 2.0 Beta2
* Added simpleVoteIntrinsics (requires GT200)

Release 2.0 Beta
* Updated to the 2.0 CUDA Toolkit
* CUT_DEVICE_INIT macro modified to take command line arguments. All samples now
  support specifying the CUDA device to run on from the command line (-device=n).
* deviceQuery sample: Updated to query number of multiprocessors and overlap
  flag.
* fluidsD3D sample: Renamed to fluidsD3D9 and updated to the new Direct3D
  interoperability API.
* multiGPU sample: Renamed to simpleMultiGPU.
* reduction, MonteCarlo, and binomialOptions samples: updated with optional
  double precision support for upcoming hardware.
* simpleAtomics sample: Renamed to simpleAtomicIntrinsics.
* simpleD3D sample: Renamed to simpleD3D9 and updated to the new Direct3D
  interoperability API.
* 7 new code samples: 
  dct8x8, quasirandomGenerator, recursiveGaussian, simpleD3D9Texture,
  simpleTexture3D, threadMigration, and volumeRender

Release 1.1
* Updated to the 1.1 CUDA Toolkit
* Fixed several bugs in common/common.mk
* Removed isInteropSupported() from cutil: OpenGL interop now works on multi-GPU
  systems
* MonteCarlo sample: Improved performance.  Previously it was very fast for
  large numbers of paths and options, now it is also very fast for 
  small- and medium-sized runs.
* Transpose sample: updated kernel to use a 2D shared memory array for clarity, 
  and optimized bank conflicts.
* 15 new code samples: 
  asyncAPI, cudaOpenMP, eigenvalues, fastWalshTransform, histogram256,
  lineOfSight, Mandelbrot, marchingCubes, MonteCarloMultiGPU, nbody, oceanFFT,
  particles, reduction, simpleAtomics, and simpleStreams

Release 1.0
* Updated to the 1.0 CUDA Toolkit.
* Added 4 new code samples: convolutionTexture, convolutionFFT2D,
  histogram64, and SobelFilter.
* All graphics interop samples now call the cutil library function 
  isInteropSupported(), which returns false on machines with multiple CUDA GPUs,
  currently (see above).
* When compiling in DEBUG mode, CU_SAFE_CALL() now calls cuCtxSynchronize() and
  CUDA_SAFE_CALL() and CUDA_CHECK_ERROR() now call cudaThreadSynchronize() in
  order to return meaningful errors. This means that performance might suffer in
  DEBUG mode.

Release 0.9
* Updated to the 0.9 CUDA Toolkit.
* Added 7 new code samples: MersenneTwister, MonteCarlo, imageDenoising, 
  simpleTemplates, deviceQuery, alignedTypes, and convolutionSeparable
* Removed 3 old code samples: convolution, vectorLoads and loadUByte. Convolution
  is replaced by convolutionSeparable and vectorLoads and loadUByte are
  replaced by alignedTypes.

Release 0.8.1 beta
* Standardized project and file naming conventions.  Several project names 
  changed as a result.
* cppIntegration output now matches the other samples ("Test PASSED").
* Modified transpose16 sample to transpose arbitrary matrices efficiently, and
  renamed it to transpose.
* Added 11 new code samples: bandwidthTest, binomialOptions, BlackScholes, 
  boxFilter, convolution, dxtc, fluidsGL, multiGPU, postProcessGL, 
  simpleTextureDrv, and vectorLoads.
* Changed object file output directory to reside within each project's 
  directory rather than in the shared bin directory.  This prevents linker 
  errors caused by different projects having files with identical names.
* Added "make clobber" mode to makefiles to support deleting project obj
  directories.
* Consolidated scan_naive, scan_workefficient, and scan_best into a single 
  "scan" sample that runs all three scan kernels and compares performance.

Release 0.8 beta prerelease 2
* Linux CUDA SDK now uses a self extracting installer (.run)
* Linux CUDA SDK samples now build correctly if CUDA Toolkit is installed to a
  path other than /usr/local
* Added matrixmul_drv sample to Linux SDK
* Added CUBINFILES mode to project Makefiles
* simpleCUFFT and simpleCUBLAS samples now work in emulation mode (make emu=1).

Release 0.8 beta prerelease 1
* Second limited release (RHEL 4 update 3 only)
* Several new samples, better release notes

Release 0.2 alpha 2
* First limited release (RHEL 3 update 4 only)
